Role Description As a Principal Data Scientist at Kainos, you will be accountable for the successful delivery of large-scale, high-impact AI solutions that leverage state-of-the-art machine learning, generative, and agentic AI technologies. You will help set the direction for AI and data science across the business, driving the adoption of modern AI development practices and scalable, cloud-native architectures at enterprise scale. - Provide technical and thought leadership, engaging with C-level and senior stakeholders to define architectural principles and strategic direction. - Foster a culture of innovation, continuous learning, and engineering excellence—both within Kainos and across the wider industry. - Lead, mentor, and develop a community of data scientists, AI engineers, and technical managers, ensuring the adoption of robust standards and responsible AI practices. - Build enduring customer relationships and proactively develop new alliances with technology partners. - Shape Kainos’ commercial AI offerings and embed commercial acumen to influence account strategies. - Ensure customers get measurable business value from AI investments. Qualifications - Proven track record of accountability for the delivery of complex, production-grade AI/ML solutions at scale. - Demonstrable experience of technical leadership in AI delivery. - Deep expertise in developing and assuring advanced AI/ML models, including time series, supervised/unsupervised learning, reinforcement learning, LLMs, and agentic AI. - Experience with the latest AI engineering approaches such as prompt engineering, retrieval-augmented generation (RAG), and orchestration of agentic AI systems. - Expertise in data engineering for AI: handling large-scale, unstructured, and multimodal data, and integrating non-traditional data sources. - Deep understanding of responsible AI principles, model interpretability, and ethical considerations, with a track record of influencing policy and standards. - Ability to communicate and negotiate with C-level and senior stakeholders, translating complex technical concepts into business value. - Experience in developing and executing account strategies, shaping commercial AI offerings, and driving business development in partnership with sales and account managers. - Demonstrated ability to build and lead high-performing teams and wider AI and data science communities. - Strong commercial acumen with a history of influencing the commercial success of AI products and solutions. Requirements - Experience with modern deep learning frameworks (e.g., PyTorch, TensorFlow), fine-tuning or distillation of LLMs (e.g., GPT, Llama, Claude, Gemini), and advanced ML libraries (e.g., scikit-learn, XGBoost). - Experience with data storage for AI, vector databases, semantic search, and knowledge graphs. - Active contribution to open-source AI projects, research publications, and industry events/websites. - Familiarity with AI security, privacy, and compliance standards (e.g., ISO 42001). Benefits - Supportive and inclusive work environment. - Opportunities for continuous learning and professional growth. - Engagement in diverse and innovative projects. Company Description Kainos is recognised as one of the UK’s leading AI and data businesses, with a decade-long track record of delivering impactful, production-grade AI solutions for clients across government, healthcare, defence, and commercial sectors. Kainos is at the forefront of AI innovation, trusted by Microsoft, AWS, and others to deliver advanced AI and data solutions at citizen scale. - 150-strong AI and Data Practice with deep expertise in machine learning, generative AI, agentic AI, and data. - Pioneers in responsible AI, having authored the UK government’s AI Cyber Security Code of Practice implementation guide. - Commitment to ethical, secure, and measurable AI deployment.

Role Description Neo.Tax is seeking a Senior Data Scientist + Machine Learning Engineer (combo role) to build and ship models and production ML systems that power our core product experiences and automate complex tax and accounting workflows. This role is hands-on and product-oriented: you will take ambiguous problems, turn them into measurable objectives, build robust solutions, and collaborate closely with engineering and product to deploy and iterate in production. We are a remote company, but we prefer to hire in time zones that can overlap with our HQ in San Francisco, CA! Responsibilities - Own ML/AI problem spaces end-to-end: - Define success metrics, create baselines, iterate on approaches, and drive projects from prototype to production. - Model development: - Build and improve models spanning classification, information extraction, entity resolution, clustering, ranking, anomaly detection, and forecasting. - LLM systems: - Design and evaluate prompt + retrieval + tool-calling pipelines; improve quality through datasets, labeling, and systematic evaluation. - Data foundations: - Define datasets, labeling strategies, and data quality checks; build features that generalize across customer contexts. - Experimentation and evaluation: - Design offline evaluations and online experiments; build dashboards and monitoring to detect regressions. - Production ML engineering: - Build and operate training/inference pipelines (batch and/or online), model serving, feature/data pipelines, and monitoring/alerting for quality, latency, and cost. - Partner with engineering: - Collaborate on productionization, scalability, reliability, latency, and cost; contribute directly to model-serving or batch pipelines as needed. - Cross-functional collaboration: - Work with product, engineering, and customer-facing teams to understand workflows and translate real customer pain into ML deliverables. - Technical communication: - Write clear specs and postmortems, document trade-offs, and communicate progress, risks, and decisions. Qualifications - MS/PhD in Computer Science, Statistics, Mathematics, or a related quantitative field, or equivalent practical experience. - 6+ years of industry experience as a Data Scientist / Applied Scientist / ML Engineer shipping ML to production (or equivalent). - Strong proficiency in Python and the modern data/ML ecosystem (NumPy/Pandas, scikit-learn, PyTorch or TensorFlow). - Strong understanding of statistical modeling, experimentation, and evaluation (metrics, confidence intervals, A/B testing, bias/variance, error analysis). - Experience building data pipelines and working with SQL and relational databases. - Experience deploying and maintaining models in production (batch or real-time), including monitoring and iteration; comfortable owning operational concerns (reliability, latency, cost). - Ability to operate with high ownership in ambiguous environments; strong communication and collaboration skills. - Ability to effectively design and implement solutions without the help of AI. - Experience with LLM evaluation, synthetic data generation, RAG, or tool-augmented agents. Bonus - Experience with information extraction and document understanding. - Experience with distributed data processing (e.g., Spark, Beam) and/or workflow engines. - Experience with GCP, AWS, or Azure. - Experience working at early-stage, venture-backed startups. Benefits - Salary range: $190,000-210,000 - Stock Option Plan (Equity) - Health Care Plans (Medical, Dental, Vision, Short-term Disability) - 90% coverage for individual + family - Health & Wellness subsidy - Retirement Plan (401k) - Paid Time Off (Vacation, Sick & Public Holidays) - Family Leave (Maternity, Paternity) - Work From Home option

Role Description The Data Scientist will combine deep analytical expertise with production-grade software development skills to build reliable, scalable solutions on our proprietary clinical intelligence platform. You will own initiatives from problem framing to model delivery and partner with software and data engineering partners to deploy into production environments. We are seeking a mission-driven Data Scientist to join our growing team and support risk adjustment strategy within our Medicare Advantage line of business. This role focuses on enhancing risk score accuracy, CMS audit preparedness (RADV), and building AI-powered tools that improve clinical documentation review and integrity. You’ll play a key role in advancing AVA, our proprietary clinical intelligence platform, by developing next-generation models that support autonomous chart review and NLP/GenAI-driven documentation analytics. This is a unique opportunity to work at the intersection of healthcare, compliance, and machine learning—transforming how we ensure both quality and regulatory alignment. Job Duties/Responsibilities - Own end-to-end solutions: translate ambiguous healthcare and operational problems into AI/ML models that deliver measurable impact by selecting methods, building data/models, deploying services, instrumenting observability, and real-world feedback loops. - Partner with Product, Engineering, and Clinical leaders to embed data science into workflows like claims processing, utilization management, provider network optimization, risk adjustment, etc. - Deliver clinical intelligence features such as autonomous chart review, disease detection, compliance forecasting, and quality analytics. - Build AI/NLP/LLM models for document understanding and information extraction, including OCR, NER, and vision models. - Advance quality and payment integrity: create models and automated QA to reduce manual review, increase accuracy, and surface documentation anomalies and audit risk. - Champion engineering excellence: internal libraries, reusable components, coding standards, code reviews, and clear documentation. - Own the lifecycle: monitoring, drift detection, alerting, and post-deployment reviews. Requirements - 2-5 years delivering production-grade data science or ML software with measurable impact. - Strong software engineering fundamentals: data structures, algorithms, modular design, API design, documentation. - Proficiency in Python plus SQL; experience building reusable packages, utilities, and CLI tools. - Applied knowledge of ML (tree ensembles, boosting, NLP/LLMs, deep learning) and experimental design. - Experience with high-volume, high-dimensional, and unstructured data; strong data quality mindset. - Experience with Git workflows, code reviews, CI/CD. - Excellent data visualization and storytelling skills. - Ability to thrive in ambiguous, fast-paced environments. Preferred - Healthcare domain experience; familiarity with CMS/Medicare Advantage operations. - Document understanding systems with OCR/NER and LLM pipelines. - Experience with NoSQL and performance optimization. - Contributions to internal frameworks or open-source; published work. Education - Required: Bachelor’s degree in Computer Science, Data Engineering or similar technical field. - Preferred: MSc/PhD in CS, Engineering, Math, Statistics, or related field. Specialized Skills - Python, SQL - Databricks (Spark, Delta Lake) - MLflow, Unity Catalog, Git, CI/CD, Docker - REST/GraphQL APIs, orchestration - NLP/LLM & Vision: OCR, NER, embeddings - Ability to communicate positively, professionally and effectively with others; provide leadership, teach and collaborate with others. - Effective written and oral communication skills; ability to establish and maintain a constructive relationship with diverse members, management, employees and vendors. Essential Physical Functions - While performing the duties of this job, the employee is regularly required to talk or hear. - The employee regularly is required to stand, walk, sit, use hand to finger, handle or feel objects, tools, or controls; and reach with hands and arms. - The employee frequently lifts and/or moves up to 10 pounds. Specific vision abilities required by this job include close vision and the ability to adjust focus. Pay Range $149,882.00 - $224,823.00 Equal Opportunity Statement Alignment Health is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, age, protected veteran status, gender identity, or sexual orientation. Disclaimer Please beware of recruitment phishing scams affecting Alignment Health and other employers where individuals receive fraudulent employment-related offers in exchange for money or other sensitive personal information. Please be advised that Alignment Health and its subsidiaries will never ask you for a credit card, send you a check, or ask you for any type of payment as part of consideration for employment with our company. If you feel that you have been the victim of a scam such as this, please report the incident to the Federal Trade Commission at https://reportfraud.ftc.gov/#/ . If you would like to verify the legitimacy of an email sent by or on behalf of Alignment Health’s talent acquisition team, please email careers@ahcusa.com .

• Lead the Build of L&W's Centralized Data Function • Stand up L&W's centralized data hub, including infrastructure, master data management, governance, data engineering, analytics engineering, and data science capability. • Define and operate the hub-and-spoke operating model that pairs centralized standards with embedded data partnership across business units and functions. • Hire, onboard, and develop the full data hub team, building a high-performing organization with a strong talent bar and low attrition. • Develop and Execute the Multi-Year Data Strategy. • Develop a multi-year data roadmap covering infrastructure, master data management, governance, and AI-aligned use-case sequencing, endorsed by the Executive Leadership Team and integrated into the broader AI transformation plan. • Translate L&W's enterprise strategy and AI ambitions into a sequenced, executable data investment plan with clear value, timing, and accountability. • Make architectural and platform decisions that establish Snowflake-as-source-of-truth, modern data tooling, and governance standards across the enterprise. • Drive Measurable Business Value. • Deliver an early, fully shipped, financially-validated win that demonstrates the value of the data function, visible to leadership, signed off by Finance, and meaningful in dollar impact. • Stand up data infrastructure that materially supports Finance and Operations in achieving their AI AEBITDA targets, including documented data dictionaries, lineage, and shared definitions. • Ensure the data infrastructure built and operated by this role drives a meaningful share of L&W's enterprise AI AEBITDA target by 2028, with Finance attestation. • Build and deliver an R&D Intelligence Layer to support portfolio and game development decision-making, partnering closely with the Head of Product. • Establish Enterprise Data Governance and Trust. • Establish and operate enterprise data governance, including policies, stewardship roles, data domains, decision rights, and an executive Data Council. • Governance must drive measurable adherence and behavior change, not just policy documentation. • Ensure data quality, security, regulatory compliance, and AI-readiness across critical data domains. • Build the trust, transparency, and shared definitions that allow data to be confidently used in decisions across Finance, Operations, and the business units. • Executive Leadership Collaboration and Strategic Influence. • Serve as a trusted partner to the Chief Strategy Officer, Executive Leadership Team, and Board on data and AI priorities. • Build coalitions with peer leaders across Technology, Finance, Operations, and the business units, driving alignment without relying on hierarchical authority. • Prepare and deliver executive-level communications, insights, and recommendations that translate data and AI strategy into business outcomes.