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Role Description At Coursera, our Data Science team is helping to build the future of education through data-driven decision-making and data-powered products. We drive product and business strategy through measurement, experimentation, and causal inference to help Coursera deliver effective content discovery and personalized learning at scale. We believe the next generation of teaching and learning should be personalized, accessible, and efficient. With our scale, data, technology, and talent, Coursera and its Data Science team are positioned to make that vision a reality. We’re looking for a knowledgeable Data Scientist - Business Analytics to support decision-making for our business and consumer leadership teams. This role will be leveraging the rich data captured by over 200M+ learners and thousands of instructors engaging on the platform to identify business trends, define and track KPIs, and deliver data-driven insights that help shape consumer strategy. This role will partner closely with business leaders across the organization to align on strategic priorities and measure business performance. Our ideal candidate possesses strong analytical and reporting skills, familiarity with trend analysis and KPI frameworks, strong business acumen, and the ability to translate analysis into actionable recommendations that inform consumer and business strategy. - Develop a deep understanding of Coursera’s business, market, and learners to identify trends and translate them into actionable insights and recommendations. - Analyze business performance, define and track KPIs, and conduct deep dives into consumer behavior and business trends. - Partner with business leaders to shape consumer strategy and inform strategic prioritization. - Support analytics teams to build dashboards and reporting systems for tracking key business and consumer metrics. - Present findings and recommendations to business leaders and cross-functional partners clearly and concisely. Qualifications - Background in economics, statistics, data science, computer science, or a related technical field. - 3+ years of experience as a data scientist, data analyst, or business intelligence analyst. - Strong SQL skills and proficiency with at least one scripting language (e.g. Python, R). - Proficiency in BI tools (e.g. Sigma, Databricks, Looker, Tableau). - Excellent communication skills, with the ability to translate complex data findings into actionable insights. - Strong attention to detail and the ability to manage multiple projects simultaneously. Requirements - 5+ years of experience using data to advise business, marketing, or consumer strategy teams. - Advanced proficiency with SQL and Python to produce clear and actionable insights. - Experience leveraging AI tools to accelerate data analysis, generate insights, or enhance analytical workflows. - Familiarity with online education platforms, specifically with platforms like Coursera and Udemy. Compensation Our job titles may span more than one career level. The starting base pay for this role is between $113,600 CAD and $142,000 CAD. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs, and location. The base pay range is subject to change and may be modified in the future. This role is also eligible for bonus, equity, and benefits.
The AI headhunter connecting elite tech sales talent with high-growth startups.
• Own data sets end to end, from conception through delivery • Partner with product, research, and deployment teams to define goals for each data set • Manage projects on the company's data platform, including onboarding speakers and coordinating QA • Work closely with engineering on bug fixes, noise reduction, and automation • Hold the quality bar, prioritizing quality over speed and volume • Problem-solve with researchers and ML engineers on how data will perform in model training
• Provide hands-on technical leadership for the three required AI/ML models using CASCOM, GCSS-Army, SAP, and other Army data. • This person must be capable of coding, evaluating, deploying, and sustaining models—not merely leading strategy discussions. • The potential use cases include GenAI/OpenAI, Copilot, SAP ECC search, document extraction, anomaly detection, equipment-readiness forecasting, maintenance forecasting, fleet automation, and supply forecasting. • Lead selection and refinement of the three baseline AI/ML use cases. • Define the mission question, data requirement, technical baseline, performance metric, and acceptance criteria for each model. • Perform data exploration, feature engineering, model development, comparative evaluation, and error analysis. • Work with the GCSS-Army SME to validate business rules and interpret model results. • Work with the Azure Data/MLOps Engineer to package, deploy, monitor, version, and sustain models. • Develop GenAI, RAG, forecasting, anomaly detection, or recommendation capabilities as selected. • Establish human-review procedures where model outputs affect operational decisions. • Prepare model cards, evaluation results, known limitations, release documentation, and retraining criteria. • Support integration of model findings into dashboards and Power Apps. • Participate in demonstrations and CASCOM production-readiness reviews. • Ability to work remotely during normal Government working hours and participate in daily status, requirements, development, and technical-review sessions. • Ability to travel to Fort Lee, Virginia, at least three times annually and report onsite more frequently if directed.
Empowering Cardiovascular Specialists to Transform Patient Care
• Support enterprise cardiovascular quality initiatives, including: MIPS/MVPs, Clinically Integrated Network, and payer-specific performance reporting. • Analyze provider-, practice-, and network-level quality performance data. • Develop and maintain dashboards, scorecards, and routine performance reports. • Identify trends, variation, and opportunities for quality improvement. • Collaborate with physician leaders and quality staff to support quality improvement initiatives. • Support Cardiovascular Associates of America’s clinical research portfolio, including: Registry-based and real-world evidence studies, Outcomes research and health services research, Investigator-initiated and industry-sponsored studies. • Assist with cohort development, data extraction, and statistical analyses. • Support study feasibility assessments and analytic plans under senior guidance. • Contribute to abstracts, manuscripts, posters, and internal research reports. • Support dissemination of research findings to inform clinical practice and strategy.
Role Description As a Data Scientist at Gainwell, you will support Gainwell's Medicaid and public sector analytics initiatives by leading advanced data science activities across complex healthcare and enterprise datasets. The role focuses on applying statistical modeling, machine learning, and scalable analytics techniques to generate actionable insights that inform business, clinical, and programmatic decisions. You will function independently within a business area while collaborating across multidisciplinary teams. At this level, the Data Scientist is expected to influence analytical approaches, mentor junior staff, and contribute to the continuous improvement of Gainwell's data science practices, tooling, and delivery standards. This role is strictly involved in data analytics, modeling, and advanced data science solution development and does not involve access to Protected Health Information (PHI), Personally Identifiable Information (PII), or any secured or confidential client data. The work is limited to analytics development, model design, and insight generation using approved and governed datasets and does not include handling or processing of sensitive health or personal information. Your role in our mission: - Lead data ingestion, cleansing, transformation, and aggregation efforts for large scale and complex datasets. - Design and implement advanced feature engineering, statistical estimation, and hypothesis testing techniques. - Develop, validate, and refine machine learning and statistical models, including time series, repeated measures, and mixed effects models. - Ensure analytical rigor by addressing overfitting, false discovery, bias, and model generalizability. - Analyze healthcare and enterprise datasets to surface complex, high impact, actionable insights that support strategic decision making. - Drive iterative model development and support continuous integration and deployment of analytics solutions. - Optimize data science solutions for performance, scalability, and production readiness. - Leverage cloud based platforms to support elastic, high volume data science workloads. - Collaborate with business stakeholders, data engineers, architects, and analysts to align analytics outputs with business objectives. - Provide technical leadership and guidance to junior data scientists and analysts. - Contribute to the definition and evolution of data science standards, best practices, and reusable analytics assets. - Clearly document analytical methodologies, assumptions, results, and recommendations. - Present insights and recommendations effectively to technical and non-technical stakeholders, including leadership audiences. Qualifications - 10+ years of experience in data science, advanced analytics, or related roles. - Expert proficiency in SQL, including complex set-based query development for large scale datasets. - Deep, hands-on experience with SQL windowing functions. - Strong understanding of database concepts such as indexing, stored procedures, and materialized views. - Advanced proficiency in Python, including object-oriented design and common machine learning libraries. - Strong knowledge of statistical methods, including time series analysis, repeated measures, mixed effects models, and hypothesis testing. - Proven experience applying machine learning techniques, including model evaluation, tuning, and lifecycle management. - Experience with Dev/Sec/Ops practices and CI/CD pipelines for analytics development and deployment. - Strong experience in performance optimization for both development and production analytics environments. - Hands-on experience using Databricks for enterprise data science workloads; Scala knowledge is a plus. - Knowledge of semi-structured and unstructured data, schema on read techniques, parsers, and NLP libraries. - Demonstrated experience deriving insights from healthcare datasets. - Experience performing data science in a major cloud environment (AWS, Azure, or GCP). Benefits - Remote working - Work life balance - Shift timing: 1pm to 10pm
Role Description In this role, you will lead the technical outcomes and rigor for both the intelligence layer that powers how Risepoint engages with students at every stage of their journey and the data engineering backbone beneath it, serving as the principal point of accountability for the domain. You will define and lead end-to-end initiatives—from strategy and architecture through cross-functional implementation and measurable outcomes—that directly shape retention, engagement, and enrollment results for thousands of students across more than 100 university partners. You will shape and execute the technical vision for and delivery of the “Next Best Experience” platform: the predictive engine that turns raw behavioral signals into personalized, timely outreach. You will build alignment across Product, Engineering and business stakeholders on technical approaches. Your decisions, technical expertise and data-driven recommendations will determine who gets reached, when, and how—translating data science into student outcomes that help working adults succeed in programs that change their lives. You will bring our mission to life by leading initiatives that make the student journey smarter and more human at the same time. Every initiative you own—from scoping a churn-risk model through deploying it into production and measuring its downstream impact—translates directly into a real person getting the support they need before they fall through the cracks. By driving cross-functional alignment and accountability across Product, Engineering, and CX teams, you will help Risepoint’s university partners serve more students more effectively. Qualifications - A proven track record of delivering measurable consumer and business impact through AI/ML initiatives. - Experience operating as a principal-level technical leader or domain authority. - 8+ years in applied machine learning or data science, ideally in education, consumer tech, personalization, or a complex behavioral domain. - Strong background in predictive analytics, recommendation systems, and experimentation. - Deep expertise in Python and SQL; proficiency with ML libraries. - Principal-level data engineering experience. - Hands-on experience taking models to production and operating them there. - Proficiency with production automation tooling. - Strong grounding in data quality, governance, lineage, and observability practices. - Excellent communicator and influencer across technical and non-technical audiences. - Bachelor’s or Master’s degree in a technical discipline. Requirements - Set direction for and lead AI/ML initiatives end-to-end. - Own accountability for delivering measurable business outcomes from each initiative. - Drive alignment and decision-making across teams. - Identify and scope net-new AI/ML opportunities. - Manage relationships with key vendors and software providers. - Build and deploy predictive models. - Lead the design and implementation of “next best action” logic. - Architect, build, and own scalable, reliable data pipelines. - Automate production workflows with orchestration tools. - Design and lead A/B testing programs. - Mentor data scientists and engineers across the team. Benefits - Equal-opportunity employer. - Supports a diverse and inclusive workforce.
SoFi helps you save, spend, earn, borrow, invest, and protect your money–all in one app. NMLS 1121636
Employee Applicant Privacy Notice Who we are: Shape a brighter financial future with us. Together with our members, we’re changing the way people think about and interact with personal finance. We’re a next-generation financial services company and national bank using innovative, mobile-first technology to help our millions of members reach their goals. The industry is going through an unprecedented transformation, and we’re at the forefront. We’re proud to come to work every day knowing that what we do has a direct impact on people’s lives, with our core values guiding us every step of the way. Join us to invest in yourself, your career, and the financial world. The role: The Compliance Senior Data Scientist will be responsible for assisting the Anti-Money Laundering Compliance program with model development, model optimization, model validation, management information reporting, AML system integration, AML data infrastructure and AML data architecture to effectively fight financial crime. Additionally, this role will also support AML governance initiatives including risk assessments and internal/external inquiries. What you’ll do: - Facilitate AML model development, implementation, optimization, assessment and validation of risk-based customer screening, transaction screening, transaction monitoring and AML customer risk rating covering multiple product lines, including banking, brokerage and lending to ensure sound risk coverage across the enterprise - Maintain, test and configure AML vendor solutions to ensure conceptually sound design, proper implementation, and acceptable model performance. - Research, compile and evaluate large sets of data to assess quality, integrity and completeness to determine suitability for AML model development. - Architect and lead the design of advanced AML models utilizing machine learning and statistical modeling methods for supervised and unsupervised learning. - Exercise flexibility in selecting model architectures, algorithms, third-party libraries, and development workflows, provided they align with project objectives and organizational requirements. - Ensure AML compliance and regulatory requirements are embedded in the model design. - Document modeling methodology, data sources, assumptions, and validation results. - Lead governance and quality control across the full AML model lifecycle including code reviews, validation of methodology, input data integrity, and performance metrics. - Ensure adherence to the organization’s established ML framework, coding conventions, documentation standards, and model risk management policies, embedding AML compliance and regulatory requirements into design and deployment. - Oversee documentation and review processes for internal model validation, external regulatory examinations, and cross-functional approvals, while supporting resolution of development blockers and coordinating with key stakeholders. - Develop governance documentation related to tuning efforts, parameter changes and data validation for AML transaction monitoring to ensure a comprehensive audit trail is maintained. - Track and report results of tuning and optimization activities and model performance to senior management. - Develop robust management information dashboards displaying real-time or near real-time AML metrics. - Partner with and advise the AML Governance Unit by providing necessary data for AML Risk Assessments, internal/external audit examinations and other regulatory requirements. What you’ll need: - Bachelor’s Degree or Master’s Degree in Statistics, Computer Science, Mathematics, Finance, Computer Science, Engineering or other relevant areas. - 3+ years of experience in the finance industry focusing on BSA/AML, OFAC, or fraud modeling/analytics. - Statistical/data analytical skills, including data quality validation, and predictive modeling experience in SQL and Python. - Knowledge of and ability to leverage traditional databases, cloud-based computing, and distributed computing. - Track record of leading AML governance-related initiatives, such as risk assessments, internal/external audits and other regulatory requirements. - Demonstrated ability to communicate effectively with all levels of the organization and across different business lines. Nice to Have: - Knowledge of AML regulations and the USA PATRIOT Act. - Familiarity with regulatory guidance on Model Risk Management (Federal Reserve SR Letter 11-7, OCC Bulletin 2011-12, FDIC FIL 22-2017, DFS504) - Experience with data visualization (e.g., Tableau) - Experience with data monitoring systems (e.g., DataDog, Monte Carlo) - Experience with cloud data infrastructure (e.g., Snowflake) - Experience with automated transaction monitoring (e.g., Verafin) - Experience with customer/transaction screening (e.g., LexisNexis) - Experience with infrastructure automation software (e.g., Terraform) - Familiarity with virtualization and containerization (e.g., Docker) - Familiarity with container orchestration (e.g., Kubernetes) - CAMS certification preferred Compensation and Benefits The base pay range for this role is listed below. Final base pay offer will be determined based on individual factors such as the candidate’s experience, skills, and location. To view all of our comprehensive and competitive benefits, visit our Benefits at SoFi page! SoFi provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion (including religious dress and grooming practices), sex (including pregnancy, childbirth and related medical conditions, breastfeeding, and conditions related to breastfeeding), gender, gender identity, gender expression, national origin, ancestry, age (40 or over), physical or medical disability, medical condition, marital status, registered domestic partner status, sexual orientation, genetic information, military and/or veteran status, or any other basis prohibited by applicable state or federal law.The Company hires the best qualified candidate for the job, without regard to protected characteristics.Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.New York applicants: Notice of Employee RightsSoFi is committed to an inclusive culture. As part of this commitment, SoFi offers reasonable accommodations to candidates with physical or mental disabilities. If you need accommodations to participate in the job application or interview process, please let your recruiter know or email accommodations@sofi.com.Due to insurance coverage issues, we are unable to accommodate remote work from Hawaii or Alaska at this time.Internal Employees If you are a current employee, do not apply here - please navigate to our Internal Job Board in Greenhouse to apply to our open roles.
Hi there! We’re Razorfish. We’ve been leading the marketing industry with our digital expertise since the start of the internet. But in 2020, we did a full reboot. What’s different? It all starts with people. Weird, wonderful, complex people - with diverse backgrounds in strategy, creative and technology. But no matter how different we are, we all have one thing in common. We believe our differences are our strength. So we push for inclusion, challenge convention and bring in new perspectives, to inspire new ideas. Because when we connect by understanding what makes people different, we can create unforgettable experiences that enrich lives. Join us at razorfish.com.
Role Description We are seeking a strategic, client-facing Associate Director to lead media analytics and measurement. This role is responsible for turning data into clear, actionable insights that drive campaign optimization, media investment decisions, and future planning. The Associate Director partners closely with Media, Strategy, and Client teams to ensure analytics moves beyond reporting to inform performance and shape growth strategy, while advancing automation and scalable measurement frameworks. Responsibilities - Lead end-to-end media analytics and measurement strategy across campaigns - Translate performance data into actionable insights that drive optimization - Develop measurement frameworks and learning agendas aligned to goals - Deliver audience, competitive, and cross-channel insights to inform strategy - Ensure dashboards and outputs are polished, intuitive, and decision-ready - Mentor team members to elevate work from reporting to insight generation Core Capabilities (Strategy, Analytics & Tools) - Leads insight-driven measurement strategies tied to business outcomes - Identifies key performance drivers, gaps, and growth opportunities across campaigns - Develops and operationalizes measurement frameworks connecting media, audience, and business KPIs - Synthesizes cross-channel data into clear insights that inform optimization and planning - Leverages tools (CM360, Meta, Adobe/GA4, Tableau/Looker, GCP, Funnel.io) to generate insights at scale - Uses automation to reduce manual reporting and accelerate speed-to-insight - Applies advanced methods (testing, attribution, MMM) when needed to solve key business questions - Ensures strong data quality, taxonomy, and governance Communication & Client Leadership - Translates complex data into clear, concise insights that drive action - Leads client conversations focused on implications, tradeoffs, and next steps—not reporting - Delivers polished, executive-ready storytelling and presentations - Acts as a trusted advisor to clients, shaping strategy through data-informed recommendations - Brings proactive insights, trends, and opportunities to clients and internal teams - Aligns analytics with media and strategy to ensure insights translate into action - Coaches team members to elevate deliverables from data output to strategic recommendations Qualifications - 7+ years of experience in media analytics, marketing analytics, or data science, preferably within a digital or paid media environment - Strong expertise in paid media measurement, KPIs, and performance optimization across channels - Proven ability to translate data into clear insights and actionable recommendations that influence business decisions - Experience developing measurement frameworks, learning agendas, and performance strategies - Advanced proficiency with analytics and visualization tools (e.g., Adobe Analytics, GA4, Tableau, Looker) - Experience with data transformation, automation, and pipeline tools (e.g., SQL, GCP, Funnel.io) - Strong communication and storytelling skills with experience presenting to senior partners or clients - Ability to lead projects and mentor teams, ensuring delivery of high-quality, data-driven outputs Benefits - Paid Family Care for parents and caregivers for 12 weeks or more - Monetary assistance and support for Adoption, Surrogacy and Fertility - Monetary assistance and support for pet adoption - Employee Assistance Programs and Health/Wellness/Comfort reimbursements to help you invest in your future and work/life balance - Tuition Assistance - Paid time off that includes Flexible Time off, Vacation, Annual Sick Days, Volunteer Days, Holiday and Identity days, and more - Matching Gifts programs - Flexible working arrangements - ‘Work Your World’ Program encouraging employees to work from anywhere - Business Resource Groups that support multiple affinities and alliances
Role Description We are currently looking for an exceptionally talented Senior Data Scientist to join our team. You will be responsible for: - Extracting insights from complex data sets. - Developing recommender systems. - Applying advanced analytical techniques to drive strategic decision-making and optimize business outcomes. Functional Responsibilities: - Designing models with a user-centric output, such as recommender systems tailored for various applications including offer design, consumer behavior prediction for purchases, and churn prediction. - Conducting exploratory data analysis to uncover hidden patterns and extract valuable insights that contribute to business growth and optimization. - Utilizing predictive modeling and forecasting techniques to support decision-making processes and improve operational efficiency. - Continuously monitoring model performance, assessing data quality, and refining models to ensure accuracy and reliability. - Staying up to date with the latest advancements in data science and technology, and identifying opportunities to apply emerging methodologies to enhance existing models and processes. Qualifications - Minimum of 4 years of professional experience in Data Science, Machine Learning, or a related field. - Strong proficiency in programming languages such as SQL, Python and experience with relevant libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn). - Deep understanding of machine learning algorithms, statistical models, and data mining techniques. - Proficient in SQL and experience working with databases and data querying. - Problem solver: proactively implements solutions to improve business. - Exceptional communication skills in English (B2-C2), including the ability to convey complex technical concepts, collaborate effectively in team environments, and deliver impactful presentations to diverse audiences. Benefits - Ownership through equity participation. - Annual company retreat. - Education bonus for continuous learning. - Company-wide winter break. - Paid time off. - Optional in-person events and meetups. - Tailored career roadmaps. - High-performance culture.
• Analyze Gremlin’s proprietary dataset of millions of chaos engineering experiments to identify failure patterns, root causes, and resilience signals across complex distributed systems • Pretraining and fine-tuning machine learning models that automatically detect, classify, and explain failures observed during chaos experiments • Build intelligent systems that deliver automated remediation recommendations, and eventually orchestration, by learning from historical experiment outcomes and system behavior • Develop scalable data pipelines and feature stores to process, enrich, and serve large volumes of experiment data for both model training and real-time inference • Collaborate closely with platform engineers and SREs to integrate AI-driven failure analysis and remediation capabilities directly into Gremlin’s core product • Apply advanced techniques, including causal inference, graph ML, time-series modeling, and reinforcement learning, to continuously improve the accuracy and actionability of automated failure analysis • Translate insights from millions of chaos experiments into AI-powered features that help customers automatically understand blast radius, pinpoint root causes, and accelerate recovery • Research and productionize novel ML approaches, including causal AI and agentic systems, that turn raw chaos experiment data into automated, reliable remediation strategies
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SQL, Python, AI/ML, Databricks, Looker, Tableau
