{"id":455587,"url":"https://alion.io/job/lynx-analytics-senior-data-scientist-us","title":"Senior Data Scientist (US)","company":{"id":175201,"name":"Lynx Analytics","domain":"lynxanalytics.com","url":"https://alion.io/company/lynx-analytics","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":75,"open_postings":5,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-23T05:54:15Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["New York, United States","San Francisco, United States","Philadelphia, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":126000,"max_usd":240000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1251},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS Bedrock AgentCore","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"DeepEval","optional":false},{"name":"Docker","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"GitHub","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Google ADK","optional":false},{"name":"JAX","optional":false},{"name":"Langfuse","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"OpenAI Agents SDK","optional":false},{"name":"Pydantic","optional":false},{"name":"Pydantic AI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Ragas","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"LangChain","optional":true},{"name":"LLMOps","optional":true},{"name":"Pytest","optional":true}],"status":"live","first_seen_at":"2026-03-19T23:40:42Z","employer_posted_date":"2026-08-20","last_verified_at":"2026-10-23T23:37:27Z","board_verified":true,"closed_at":null,"days_open":218,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":217},"description":"ROLE SUMMARY\nWe are looking for a Senior Data Scientist to lead complex data science engagements that combine traditional statistical modelling with Generative AI. You will work hands-on with very large datasets across disparate systems and formats, translate ambiguous business problems into rigorous analytical solutions, and present those solutions clearly to C-level stakeholders. This is a delivery-first role with a fast track into technical leadership: alongside your own project work, you will help guide junior data scientists and shape how Lynx builds and ships data science solutions.\nKEY RESPONSIBILITIES\nSolution Design & Delivery\nDesign and deliver end-to-end solutions for defined data science problems, combining classical modelling, data transformation, and Generative AI / LLM techniques.\nWork hands-on with very large datasets across disparate stores and formats, from ingestion and transformation through to modelling and validation.\nApply statistical and machine learning methods to business problems such as customer retention, campaign management, and commercial performance optimisation.\nClient Communication & Leadership\nPresent results and prepare client-ready materials for project stakeholders, including C-level audiences, translating technical work into clear business narratives.\nLead smaller data science workstreams, with support from internal leadership and the PMO, including day-to-day guidance for junior team members.\nPartner with delivery and account teams to scope problems, set realistic timelines, and manage stakeholder expectations.\nKnowledge Building\nCreate reusable documentation, presentations, and code libraries during projects so future engagements can build on prior work.\nParticipate in internal education, research, and knowledge-sharing initiatives that raise the technical bar across the practice.\nSKILLS, QUALIFICATIONS AND EXPERIENCE\n8+ years of overall experience in data science, with a track record of leading analytical workstreams independently.\nDegree in Mathematics, Statistics, Economics, Computer Science, Engineering, or a related field; MSc or PhD preferred.\nSolid grounding in probability theory, statistics, and core data science algorithms, with applied experience in areas such as customer retention and campaign management.\nStrong hands-on proficiency in Python for data analysis, modelling (PyTorch, TensorFlow, or JAX), and productionising code.\nStrong SQL, and comfort working across common data stores (relational, columnar/warehouse, and vector databases).\nGit and GitHub proficiency, including branching workflows and code review; experience with GitHub Actions (or equivalent CI/CD) preferred.\nHands-on experience designing and building agentic LLM applications - tool calling, multi-step orchestration, and state management - using at least one modern framework (e.g., LangGraph, Pydantic AI, AWS Bedrock AgentCore, Google ADK, or the OpenAI Agents SDK), beyond simple prompt-and-response use of LLM APIs.\nPreferred: practical depth in one or more of MCP-based tool integration, RAG and embedding pipelines (including vector stores), model fine-tuning and RL-based post-training, and LLM guardrails and evaluation (e.g., Ragas, DeepEval, Langfuse, or similar).\nExperience with at least one major cloud platform (AWS, GCP, or Azure); Docker and basic containerised deployment preferred.\nComfortable working with very large, complex datasets residing in different data stores and formats.\nExcellent verbal and written communication skills, with strong data visualisation ability and experience presenting to senior, non-technical stakeholders.\nDemonstrated leadership potential and the presence to guide junior team members and represent the company with clients.\nNice to have:Software engineering hygiene (preferred): typed Python (Pydantic), testing with pytest, packaging, and dependency management (uv).\nExperience shipping LLM applications to production, including observability and cost/latency management (e.g., Langfuse, Phoenix, or similar LLMOps tooling).\nExperience in the life sciences industry is preferred.\n\nKEY COMPETENCIES\nExecutive Communication: Translates complex Data Science solutions into plain language for C-level and non-technical stakeholders.\nTechnical Depth: Brings rigorous statistical and modelling judgement, paired with fluency in modern GenAI/LLM approaches.\nDiscretion & Integrity: Handles sensitive client and internal information with professionalism and sound judgement.\nLeadership & Charisma: Guides junior colleagues day to day, even without a formal management title, and takes pride in their growth.\nCollaboration: A team player who builds strong working relationships across delivery teams, PMO, and clients.\nWHY YOU WILL LOVE IT HERE\nWork on real-world AI and advanced analytics solutions with measurable business impact.\nCollaborate with a global team of engineers and data scientists.\nExposure to diverse industries, modern cloud platforms, and cutting-edge AI technologies.\nA collaborative culture that values real outcomes.\nHigh ownership, zero micromanagement.\nRapid learning opportunities and diverse challenges.\nFlat organisational hierarchy with high visibility and accessibility to our 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