This page tracks remote data scientist openings that are location-eligible for Delaware.
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• Build and evaluate classification or regression models using Python and standard ML libraries • Engineer features from claims, clinical, or operational datasets • Clean, transform, and profile datasets; document data-quality findings and assumptions • Write SQL against relational databases to extract and shape analytical inputs • Build lightweight APIs or endpoints to surface model outputs (e.g., FastAPI) • Contribute to version-controlled, reproducible workflows using Git • Document pipelines so teammates can extend and audit your work • Assist with experiments at the intersection of ML and healthcare workflows
Thermo Fisher Scientific is a global biotechnology product development company whose mission is to make the world healthier, cleaner, and safer. Thermo Fisher Scientific leads a gl
Title: Data Reviewer Location: Greenfield Indiana United States of America Full time This is a fully remote role supporting our customer’s site in Greenfield, IN. We welcome applicants from all locations within the US. Must be legally authorized to work in the United States without sponsorship. Must be able to pass a comprehensive background check, which includes a drug screening. At Thermo Fisher Scientific, you’ll discover meaningful work that makes a positive impact on a global scale. Join our colleagues in bringing our Mission to life - enabling our customers to make the world healthier, cleaner and safer. We provide our teams with the resources needed to achieve individual career goals while taking science a step beyond through research, development and delivery of life-changing therapies. With clinical trials conducted in 100+ countries and ongoing development of novel frameworks for clinical research through our PPD clinical research portfolio, our work spans laboratory, digital and decentralized clinical trial services. Your determination to deliver quality and accuracy will improve health outcomes that people and communities depend on – now and in the future. Our PPD® Laboratory Services team has a direct impact on improving patient health through the expertise of scientists, industry thought-leaders and therapeutic experts. As the world leader in serving science, our laboratory professionals bring their commitment to accuracy and quality to deliver groundbreaking innovations. Discover Impactful Work: As a Data Reviewer, your role will be to perform review of a variety of routine and complex analytical analysis experiments that are conducted on pharmaceutical and biopharmaceutical compounds with various formulations and/or biological matrices. Ensure analyses are following validated or experimental analytical procedures, and compare results to methodology, protocol and product specifications, Standard Operating Procedures (SOPs) acceptance criteria, and Good Manufacturing Practices (GMP). Review data entered into databases and reports and monitors the quality of the laboratory data. Possess a thorough understanding of laboratory procedures and can reliably conduct complex analysis review independently. What You’ll Do: - Evaluate laboratory data for compliance with analytical methods, client directives, and SOPs. - Review sample results for scientific soundness, completeness, accurate representation of the data, and final reported results. - Communicate with laboratory staff to proactively maintain the quality of laboratory documentation. - Deliver review findings noting deficiencies within the analytical data or reports in a clear and concise manner. - Escalate significant deficiencies to the project leader or supervisor for assessment. - Facilitate conversations with lab staff on best documentation practices and addressing quality findings. - Advocate for quality and review process changes. - Identify and support process improvement initiatives. - Perform other duties as assigned. Education and Experience: • Bachelor's degree (Life Sciences degree preferred) or equivalent and relevant formal academic / vocational qualification • Previous experience to provide the knowledge, skills, and abilities to perform the job (comparable to 2+ years). Knowledge, Skills and Abilities: - Thorough knowledge of SOPs and Federal Regulations to include GLP and GMP - Thorough knowledge of chromatography may be required. - To demonstrate behaviours which align to the 4i Values of Thermo Fisher - Strong technical knowledge including an understanding of laboratory procedures, methodology, - and standards - Strong verbal and written communication skills - Strong attention to detail - Ability to balance time and remain focused on work to meet goals - Ability to train staff on basic review techniques - Ability to independently review laboratory reports and analytical methods - Ability to deal with multiple and changing priorities - Ability to provide clear and concise feedback and/or documentation of results - Ability to work in a collaborative team environment - Ability to work efficiently in a remote capacity Benefits We offer competitive remuneration, annual incentive plan bonus, healthcare, and a range of employee benefits. Thermo Fisher Scientific offers employment with an innovative, forward-thinking organization, and outstanding career and development prospects. We offer an exciting company culture that stands for integrity, intensity, involvement, and innovation!
We connect everyone with their past so they can discover, preserve, and share their unique family stories.
• Innovate with State-of-the-Art AI: Implement cutting-edge AI solutions for key Document Understanding tasks such as OCR/HTR, transcription, Named Entity Recognition (NER), Relation Extraction (RE), Coreference Resolution, Summarization, and Knowledge Graphs working with diverse genealogical and historical collections spanning newspapers, city directories, family history books, and vital records (i.e., birth, marriage, & death records). • Analyze and Optimize Multi-Modal Models: Evaluate the performance of multi-modal models in zero-shot and few-shot learning scenarios for comprehensive document understanding. • Architect Agentic Systems: Design and implement multi-agent workflows using frameworks like LangChain, LangGraph, CrewAI, or AutoGen to automate complex multi-step reasoning tasks in historical document analysis. • Evaluation & Observability: Establish "LLM-as-a-Judge" frameworks and use tools like Arize Phoenix, DeepEval, or RAGAS to monitor for hallucination, drift, and bias. • Collaborate on Cloud Deployment: Partner closely with ML Ops and Data Science Engineers to seamlessly deploy datasets, models, and pipelines in cloud environments. • Communicate Insights Effectively: Clearly and confidently present your findings, deliverables, and proposed solutions to technical and non-technical audiences, including teams, stakeholders, and executives.
At Ford Motor Company, we believe freedom of movement drives human progress. We also believe in providing you with the freedom to define and realize your dreams. With our incredible plans for the future of mobility, we have a wide variety of opportunities for you to accelerate your career potential as you help us define tomorrow’s transportation.
Role Description The Senior Data Scientist on the Credit AI team at Ford Credit will lead the development and deployment of advanced AI and machine learning solutions that improve customer experience, reduce risk, and drive operational efficiency. This role focuses on delivering scalable, production-ready solutions across conversational AI, fraud detection, forecasting, and intelligent automation initiatives while partnering closely with engineering, product, and business stakeholders. As a Senior Data Scientist within the Credit AI organization, you will play a critical role in shaping and delivering AI-driven solutions that support strategic business priorities across Ford Credit. You will work across a diverse portfolio of initiatives, including: - Conversational AI solutions for customer representatives - Fraud detection and risk analytics - Forecasting and predictive modeling - AI agents that automate business workflows and accelerate software development processes This role requires strong expertise in machine learning, statistical modeling, generative AI, and production AI systems. You will collaborate with cross-functional teams to: - Translate business challenges into scalable technical solutions - Develop and validate models - Ensure successful deployment into production environments - Establish best practices around model governance, monitoring, explainability, and responsible AI Success in this role will be measured through measurable business outcomes such as: - Reduced fraud losses - Improved forecast accuracy - Enhanced customer support efficiency - Increased automation effectiveness What you'll do: - Design, develop, validate, and deploy machine learning and AI solutions for business-critical applications - Build scalable predictive models, anomaly detection systems, forecasting solutions, recommendation systems, and generative AI applications - Develop conversational AI and agent-assist solutions leveraging LLMs, NLP, and retrieval-augmented generation (RAG) techniques - Create intelligent AI agents for business workflow automation and SDLC acceleration initiatives - Develop and optimize fraud detection models using supervised and unsupervised machine learning techniques - Analyze structured and unstructured datasets to identify trends, patterns, risks, and business opportunities - Partner with engineering teams to productionize AI/ML solutions and integrate them into enterprise applications and workflows - Develop reusable ML pipelines, feature engineering frameworks, and model monitoring capabilities - Monitor model performance, drift, reliability, and operational effectiveness in production environments - Collaborate with product managers, engineers, business stakeholders, and risk/compliance teams to define requirements, success metrics, and implementation strategies - Translate technical insights and analytical findings into clear business recommendations and executive-level communications - Ensure AI and machine learning solutions comply with data governance, privacy, security, and regulatory standards - Develop documentation supporting model explainability, validation, monitoring, and audit readiness - Promote responsible AI practices, including fairness, transparency, and risk mitigation - Mentor junior team members and contribute to technical standards, best practices, and continuous improvement initiatives Qualifications - Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field - 5+ years of experience developing and deploying machine learning or AI solutions in production environments - Strong programming experience in Python and experience with ML frameworks such as scikit-learn, PyTorch, TensorFlow, or similar - Experience building predictive models, forecasting solutions, anomaly detection systems, NLP applications, or generative AI solutions - Experience with large language models (LLMs), prompt engineering, retrieval-augmented generation (RAG), or conversational AI systems - Strong SQL and data manipulation skills with experience working on large-scale datasets - Experience with cloud platforms such as AWS, Azure, or GCP - Understanding of MLOps concepts including model deployment, monitoring, versioning, and CI/CD workflows - Strong analytical, problem-solving, communication, and stakeholder management skills Requirements - Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field (preferred) - Experience in financial services, credit risk, fraud analytics, or regulated industries (preferred) - Experience with AI agents, orchestration frameworks, or automation platforms (preferred) - Experience with model explainability and governance tools such as SHAP or LIME (preferred) - Knowledge of software engineering workflows and developer productivity tooling (preferred) - Experience mentoring or leading technical teams (preferred) Benefits - Immediate medical, dental, vision and prescription drug coverage - Flexible family care days, paid parental leave, new parent ramp-up programs, subsidized back-up child care and more - Family building benefits including adoption and surrogacy expense reimbursement, fertility treatments, and more - Vehicle discount program for employees and family members and management leases - Tuition assistance - Established and active employee resource groups - Paid time off for individual and team community service - A generous schedule of paid holidays, including the week between Christmas and New Year’s Day - Paid time off and the option to purchase additional vacation time
We deliver care that people love. Members can talk with doctors or counselors 24/7 via app, website or phone.
• Design and build metrics, experiments, projections, and predictive models • Write clean, efficient code in Python and SQL • Collaborate with business stakeholders to propose data-driven solutions • Test and refine data models and solutions
Based in West Lafayette, Indiana, Purdue University is a world-renowned institution of higher education offering more than 200 undergraduate and graduate degree
Job Title: Purdue Global Full-Time Faculty, Science (Remote) Location: United States Job Description: Req Id: 42207 City: Remote/Virtual Job Description: Our Opportunity: The Full-Time Faculty role will provide support to Purdue University Global's School of Multidisciplinary and Professional Studies. Building on Purdue University's mission to provide greater access to affordable, high-quality education, Purdue University Global is a public, nonprofit institution offering a world-class education online. Job Summary: Full-time faculty may have responsibilities in four areas: teaching, service, scholarship, and professional development. The amount of time spent in each area is determined on an annual basis, after discussion between the faculty member and their Program Head, and may vary from year to year. The workload is based on the needs of the Department and School, consistent with the university's mission as a teaching institution. While the majority of faculty typically spend the greatest percentage of their time teaching, there is the opportunity in a given year for the percentage of time in any category to change. Teaching responsibilities may include both undergraduate and graduate courses. The number of courses assigned will consider credentials, program needs, course types, the number of different preparations, and the number of students served. Service will typically include participating in and/or taking leadership roles in Purdue Global department or institution-wide governance committees. Service examples also include course lead, training and mentoring other faculty, facilitating student-focused organizations or activities, the evaluation of student portfolios, curriculum work, vacation relief for colleagues, and leadership in professional organizations. In limited cases, specific service activities may be assigned additional compensation. Scholarship activities vary with the interests of the individual faculty member and may not be required. Scholarship activities should be developed in consultation with the Dean, Associate Dean or Program Head. Examples of PG scholarship activities include developing and implementing solutions or new approaches to academic and professional challenges, as well as publishing articles related to research within the discipline or profession and/or presenting at academic or professional meetings. Some professional development (8 hours) is required for all faculty over the course of a year. Faculty development beyond these minimum expectations may be considered part of an annual workload, if determined appropriate by the Academic Program Head or requested by the faculty member and approved by the Program Head. A balance of these four responsibilities is intended to sustain the academic goals of the university to serve adult learners, and advance the professional goals of the faculty, while creating a supportive learning environment that provides students with a clear path to graduation. Teaching is typically the primary assignment for a Purdue Global faculty member, with service, scholarship and additional professional development activities comprising the balance of the annual workload. If no assignments are made in service and scholarship, a faculty member whose only assignment is teaching would not exceed 16-18 courses taught in a year. What to expect in this role: - Provides a student-centered learning environment which enables students to attain success. - Reports concerns regarding student academic progress to Success Coaches or Academic Advisors, as appropriate. Refers students with questions regarding financial aid, academics, attendance, and personal issues or concerns to the appropriate departments. - Actively assists the University in retention and/or outreach efforts, which may include directly contacting students who are not engaged in the course or absent per department or campus guidelines. - Maintains open and timely communication with students and the university via Purdue Global email. Reports student behavioral and Code of Student Conduct issues. - Participates in institutional assessment and assists Chairs and Deans to develop and implement new programs as assigned. - Regularly attends and participates in faculty meetings and continuous improvement sessions. Serves on university committees as assigned. - Remains current with trends, techniques, and advances in technology that are applicable to the program. Incorporates creative instructional strategies and/or learning activities. - Maintains and submits accurate and timely reports for student grades. - Follows university style and branding guidelines for materials that are created for students. Providing students with materials that always meet accessibility standards. Experience: - Minimum requirements: Master's degree in Physics; PhD preferred. - Minimum 2- 4 years related subject-matter work experience; online teaching experience preferred. What we're looking for: - Exceptional computer skills using Microsoft Office Suite, Google applications and Zoom meeting technology. Experience with Brightspace education software is preferred. - Excellent communication, organizational and time management skills, and with the ability to work independently with minimal supervision. - Ability to work effectively in a remote environment with minimal supervision. - Capable of building strong working relationships across teams, departments and Schools. - Ability to handle sensitive and confidential information with discretion. Additional Information: - The target salary for this position is $64,000, learn more about our benefits HERE - Purdue University Global will not sponsor employment authorization for this position. - A background check will be required for employment in this position. - We ask that our remote employees have access to a reliable internet connection and a dedicated, properly equipped workspace that is free of distractions. You may wish to review the Purdue Virtual Meeting Professional Standards. - When applying for a faculty position at Purdue University Global you will be asked to provide an unofficial transcript and if hired will be required to provide an official transcript. - FLSA: Exempt (Not Eligible For Overtime) - Purdue Global is an EEO/AA employer. Our goal is to recruit and retain talent from a broad pool of applicants. Purdue Global celebrates a variety of perspectives, experiences, and skills to support a success-focused environment for employees and students. Employment decisions are based on qualifications, merit, and business needs. All are encouraged to apply.
Role Description As a Senior Data Scientist, reporting to the Head of Data Science in our Data & Analytics team, your purpose will be to build and own machine learning powered features that directly improve our customer app and marketplace experience. - Work on customer-facing problems such as: - Search and ranking - Recommendations - Segmentation - Lifecycle modelling including activation, churn, and reactivation - Lead scoring - Personalization - Operate end to end, from shaping ambiguous product ideas into clearly defined problems, through modelling and experimentation, to deploying and monitoring production systems. - Take real ownership, which may include: - Contributing to data pipelines - Partnering closely with engineers on real-time systems - Ensuring models are robust, scalable, and measurable against business KPIs such as conversion, retention, and revenue. - Collaborate cross-functionally with Product, Engineering, Marketing, and Pricing, acting as both a technical expert and a structured problem-solver. Qualifications - Proven experience delivering machine learning solutions end to end in production, ideally in a marketplace or customer-facing product environment. - Strong experience with behavioural and customer data, including segmentation, lifecycle modelling, scoring, search, or recommendation systems. - Strong Python and SQL skills, with the ability to work across modelling and data preparation. - Experience working with cloud platforms, GCP preferred and AWS also welcome, and understanding how data pipelines are built and maintained. - Demonstrated ability to define problems clearly, engage stakeholders, and communicate impact in commercial environments. - Conversational-level English language skills. Requirements - Experience applying NLP and LLMs to real product use cases. Generative AI is a plus but not the whole role. - Experience working with real-time or near real-time systems. - Exposure to pricing or marketing optimisation problems. Benefits - This is a fully remote position. We take pride in being a globally distributed team. - A generous holiday allowance of 26 days plus public holidays. - Access to a global learning and development program, wellness benefits, and discounts across partner platforms.
We are on a mission to unlock the world's best food creators and bring their dishes to the doorstep of the masses.
• Define and execute the product data science strategy, identifying opportunities where ML and predictive analytics can unlock step-change improvements in customer experience and business outcomes • Partner closely with Product, Growth, Engineering, and UX leadership to influence product roadmap with data-driven insights and forward-looking ML capabilities • Act as a thought leader on emerging data science techniques (personalization, recommendation systems, causal inference, generative AI) and their application to product problems • Translate complex product challenges into clear data science problems with measurable success criteria • Own the end-to-end ML lifecycle for product use cases: problem framing, feature development, model training, deployment, monitoring, and iteration • Partner with the Growth Data Science & Analytics team to align experimentation, measurement, and modeling efforts into a cohesive end-to-end data science ecosystem. • Build, mentor, and scale a high-performing product data science team capable of delivering both strategic insights and production ML systems • Foster a culture of innovation, experimentation, and continuous learning within the data science organization • Create career development pathways that attract and retain top data science talent • Collaborate with Analytics Engineering to ensure seamless model deployment and monitoring • Own and evolve personalization and recommendation systems that drive engagement and conversion across the customer journey • Design and implement robust experimentation frameworks that enable rapid, high-quality product testing and learning • Develop causal inference methodologies to understand true incrementality of product changes. • Ensure models are observable, explainable where needed, and continuously improved post-launch • Define product success metrics and measurement frameworks that align with business objectives • Build scalable dashboards and monitoring systems that provide real-time visibility into product performance • Conduct deep-dive analyses on user behavior patterns to uncover opportunities for product optimization
Role Description As a Lead Data Scientist at Lennar, you will design, build, and deploy advanced models and AI agents that shape how Lennar prices, sells, and personalizes experiences for customers across 40+ divisions. You’ll work end-to-end—from research and experimentation to production deployment and monitoring—delivering measurable business impact in pricing, sales, operations, and customer engagement. Your Responsibilities on the Team: - Design, build, and deploy autonomous AI agents using frameworks like Amazon Bedrock and AgentCore to solve business problems in pricing, sales, operations, and customer interactions. - Apply machine vision and feature extraction on home attributes (photos, plans, finishes) to inform premium pricing and personalization strategies. - Engineer and maintain data pipelines and systems supporting all models and agents, ensuring scalability and reliability. - Integrate agents with enterprise systems and protocols (MCP servers, A2A protocol, internal APIs). - Design and run experiments (A/B tests, multi-armed bandits, uplift models) to measure and optimize model and agent performance. - Ensure observability and reliability of deployed agents, including logging, evaluation, monitoring, and drift detection. - Proactively gather feedback from stakeholders and adapt solutions for adoption and measurable impact. - Translate complex data science and statistical concepts into clear recommendations, stories, and visualizations for executives and non-technical audiences. - Favor incremental, explainable solutions that deliver quick wins and scale over time. - Drive experimentation with new tools and approaches, ensuring robustness, governance, and scalability in production deployments. - Share learnings with the broader team to raise the bar on data science and agentic development across the organization. - Manage timelines and expectations transparently with both the data science team and business stakeholders. Qualifications - Bachelor’s or Master’s degree in Statistics, Economics, Math, Computer Science, Data Science, Machine Learning, or related field (or equivalent experience). - 5+ years of relevant experience (1+ with PhD, 3+ with MS) as a data scientist, ML engineer, or applied AI developer delivering production-ready models and systems. - Strong proficiency in Python and SQL, with experience owning the full data science stack (data pipelines + models + deployment). - Hands-on experience with AI development frameworks (LangChain, Strands, Amazon Bedrock, AgentCore, or equivalent). - Experience with experimentation frameworks (A/B testing, uplift modeling, multi-armed bandits, causal ML). - Exposure to machine vision techniques (CNNs, transfer learning, embeddings) and NLP techniques (embeddings, transformers, prompt engineering). - Understanding AI agent observability (evaluation frameworks like LangFuse, RAGAS, Weights & Biases, custom monitoring). - Experience with system integrations: APIs, A2A protocol, MCP servers, orchestration pipelines. - Comfort working with large-scale, imperfect real-world datasets and making progress despite complexity. - Strong engineering skills: ability to design and maintain production pipelines, microservices, and scalable systems. - Proven ability to navigate ambiguity, rapidly prototype, and move solutions into production. - Collaborative communicator who can align technical solutions with business priorities across diverse stakeholders. - Bonus: experience with RAG pipelines, LLM fine-tuning, RLHF, multi-agent orchestration, feature stores, survival analysis/churn modeling, and attribution modeling. Requirements - This is primarily a sedentary office position which requires the incumbent to have the ability to operate computer equipment, speak, hear, bend, stoop, reach, lift, and move and carry up to 25 lbs. Finger dexterity is necessary. Benefits - Base compensation offered for this position to range from an annual salary of $152,600.00 - $190,700, subject to adjustment based on business-related factors. - This position may be eligible for bonuses. - This position may be eligible for commissions. - Comprehensive health insurance plans, including Medical, Dental, and Vision coverage. - 401(k) Retirement Plan with a $1 for $1 Company Match up to 5%. - Paid Parental Leave and an Associate Assistance Plan. - Education Assistance Program and up to $30,000 in Adoption Assistance. - Up to three weeks of vacation annually, alongside generous Holiday, Sick Leave, and Personal Day policies. - New Hire Referral Bonus Program and significant Home Purchase Discounts. - Unique opportunities such as the Everyone’s Included Day.
Role Description The Data Scientist II role sits on a team where data science products are core to what we build. We develop computer vision systems that analyze real estate properties, valuation models that price homes, generative AI that powers smarter property search experiences, and traditional machine learning that drives business decisions. We are also investing in systems that can reason, plan, and operate with increasing autonomy. This is a mid-level, hands-on individual contributor role for someone who wants end-to-end ownership. We’re looking for someone who is a critical thinker and can work independently solving ambiguous problems with sound judgment and minimal direction, while remaining highly collaborative with teammates. You take full ownership of your work, demonstrating accountability from start to finish, and are self-motivated in driving projects forward without constant oversight. You communicate clearly, listen actively, and remain open to feedback and continuous growth. As part of a small, agile team, you will contribute end-to-end, rolling up your sleeves to execute rather than operate at a purely strategic level. You are action-oriented, enjoy solving problems, and bring a positive, engaging presence to the workplace—valuing both strong relationships and a sense of fun at work. Candidates should be prepared to share examples of production-grade models or systems they have owned end-to-end, including what they learned from deployment, monitoring, and iteration. Real estate or mortgage experience is not required; curiosity about how people search for, buy, finance, and value homes is helpful. Primary Duties and Responsibilities - Analyze data to support (or disprove) a thesis – You'll dig into data, form hypotheses, and let evidence guide your conclusions. We value intellectual honesty over confirmation bias. - Select and implement the right tools for the job – Not every problem needs a transformer. Some problems just need a well-tuned gradient boosting model. You'll know the difference. - Build, train, test, and validate models – From algorithm selection to hyperparameter tuning to rigorous evaluation. You'll need solid grounding in math and statistics to evaluate model performance and defend your choices. - Engineer models into production – This isn't research for research's sake. Your models need to run reliably in the real world, on real infrastructure, serving real customers. - Document your work – Future you (and your teammates) will thank you. We maintain clear documentation for models, testing protocols, and decision rationale. - Monitor and improve models in production – Models drift. Data changes. You'll keep watch and know when it's time to retrain, rebuild, or rethink. - Explore agentic and reasoning systems – We're investing in semi-autonomous systems that can plan and act. You'll help us figure out what's hype and what's actually useful. - Perform other duties as assigned or apparent. Qualifications - Degree Requirement: Bachelor's Degree or equivalent experience - Work Experience: 2 or more years of prior work-related experience - 2-5+ years of hands-on AI experience including working with LLMs (GPT, Claude, Qwen, or similar) via API/SDK and building and deploying ML or DL models in production environments. - Core to success in this role is the ability to evaluate model performance beyond surface metrics and explain uncertainty clearly. This requires a strong scientific foundation in linear algebra, calculus, probability, and statistical inference. - Understanding of prompt engineering, RAG architectures, fine-tuning approaches, and embedding models. - Strong command of supervised and unsupervised learning techniques: regression, classification, clustering, dimensionality reduction, ensemble methods. - Ability to evaluate LLM outputs critically and design appropriate guardrail systems. - Familiarity with tokenization, context windows, and inference optimization. - Deep learning expertise including CNNs, RNNs/LSTMs, transformers, and attention mechanisms. - Practical experience implementing Reinforcement Learning algorithms: Q-learning, policy gradients, actor-critic methods, or multi-armed bandits. - Understanding of reward shaping, exploration vs. exploitation tradeoffs, and temporal difference learning. - Ability to evaluate and define the appropriate model for each problem based on business requirements. - Experience with model testing frameworks, model evaluation, validation strategies, and model documentation. Requirements - Strong Snowflake/SQL skills and experience working with large datasets. - Proficiency with pandas, NumPy, and data manipulation at scale. - Experience with data quality assessment, cleaning, and validation. - Proficiency writing clean, maintainable, production-quality Python code. - Familiarity with ML pipelines, feature engineering, and data preprocessing at scale. - Understanding of model serving patterns: batch inference, real-time APIs, streaming. - Experience deploying to production and maintaining models over time. - Working experience with AWS services: Bedrock, SageMaker, Lambda, S3, EC2, Step Functions, CloudWatch, EKS. - Familiarity with containerization (Docker) and orchestration basics. - Experience with infrastructure-as-code using CDK or terraform. - Git version control and collaborative development practices. - Altassian suite of JIRA and Confluence, Slack for communications. - Jupyter notebooks for exploration, Python packages for production. - PyTorch and/or TensorFlow. - scikit-learn, XGBoost, LightGBM, autogluon, Catboost. - MLflow, Weights & Biases, or similar experiment tracking. Benefits - Competitive Compensation: anticipated base salary from $98,000 to $148,000 based on skills and experience. This position is eligible to participate in an annual incentive program. - Rest and Relaxation: This role is eligible for 25 days of paid time off annually, which is prorated in the year of hire based on hire date. In addition, based on your hire date, you will be eligible for 9 paid holidays + 2 floating holidays. Parental leave is also offered as an opportunity for all new parents to embrace this exciting change in their lives. - Comprehensive Health Benefits: Multiple medical plan choices, including HSA and FSA options, dental, vision, and basic life insurance. - Prepare for your Future: 401(k) with a top of market company match (did we mention the company match is immediately vested?!) and an opportunity to participate in Radian’s Employee Stock Purchase Plan (ESPP). - Homebuyer Perks: Our Homebuyer Perks program helps employees navigate the home searching, buying, selling, and refinancing processes and provides valuable financial benefits to encourage, enable, and support home ownership. - Additional Benefits: To learn more about our benefits offerings, visit our Benefits Page.
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Python, SQL, AWS, AI/ML, Observability/Monitoring, AI
