# Senior Data Scientist (US) at Lynx Analytics Senior Data Scientist (US) at Lynx Analytics in New York, USA; Philadelphia, Pennsylvania, United States; San Francisco, California, United States. Posted March 19, 2026. - Company: Lynx Analytics - Location: New York, USA; Philadelphia, Pennsylvania, United States; San Francisco, California, United States - Experience: 8+ years - Posted: 2026-03-19 - Apply: https://job-boards.greenhouse.io/lynxanalytics/jobs/8470862002 - Source: https://jobmagic.ai/jobs/senior-data-scientist-us-lynx-analytics-deftq2 ## Requirements - 8+ years of overall experience in data science, with a track record of leading analytical workstreams independently. - Degree in Mathematics, Statistics, Economics, Computer Science, Engineering, or a related field; MSc or PhD preferred. - Solid grounding in probability theory, statistics, and core data science algorithms, with applied experience in areas such as customer retention and campaign management. - Strong hands-on proficiency in Python for data analysis, modelling (PyTorch, TensorFlow, or JAX), and productionising code. - Strong SQL, and comfort working across common data stores (relational, columnar/warehouse, and vector databases). - Git and GitHub proficiency, including branching workflows and code review; experience with GitHub Actions (or equivalent CI/CD) preferred. - Hands-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. - Preferred: 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). - Experience with at least one major cloud platform (AWS, GCP, or Azure); Docker and basic containerised deployment preferred. - Comfortable working with very large, complex datasets residing in different data stores and formats. - Excellent verbal and written communication skills, with strong data visualisation ability and experience presenting to senior, non-technical stakeholders. - Demonstrated leadership potential and the presence to guide junior team members and represent the company with clients. - Nice to have: Software engineering hygiene (preferred): typed Python (Pydantic), testing with pytest, packaging, and dependency management (uv). - Experience shipping LLM applications to production, including observability and cost/latency management (e.g., Langfuse, Phoenix, or similar LLMOps tooling). - Experience in the life sciences industry is preferred. ## Description **ROLE SUMMARY** We 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.   **KEY RESPONSIBILITIESSolution Design & Delivery** - Design and deliver end-to-end solutions for defined data science problems, combining classical modelling, data transformation, and Generative AI / LLM techniques. - Work hands-on with very large datasets across disparate stores and formats, from ingestion and transformation through to modelling and validation. - Apply statistical and machine learning methods to business problems such as customer retention, campaign management, and commercial performance optimisation. **Client Communication & Leadership** - Present results and prepare client-ready materials for project stakeholders, including C-level audiences, translating technical work into clear business narratives. - Lead smaller data science workstreams, with support from internal leadership and the PMO, including day-to-day guidance for junior team members. - Partner with delivery and account teams to scope problems, set realistic timelines, and manage stakeholder expectations. **Knowledge Building** - Create reusable documentation, presentations, and code libraries during projects so future engagements can build on prior work. - Participate in internal education, research, and knowledge-sharing initiatives that raise the technical bar across the practice.   **SKILLS, QUALIFICATIONS AND EXPERIENCE** - 8+ years of overall experience in data science, with a track record of leading analytical workstreams independently. - Degree in Mathematics, Statistics, Economics, Computer Science, Engineering, or a related field; MSc or PhD preferred. - Solid grounding in probability theory, statistics, and core data science algorithms, with applied experience in areas such as customer retention and campaign management. - Strong hands-on proficiency in Python for data analysis, modelling **(PyTorch, TensorFlow, or JAX)**, and productionising code. - Strong SQL, and comfort working across common data stores (relational, columnar/warehouse, and vector databases). - Git and GitHub proficiency, including branching workflows and code review; experience with GitHub Actions (or equivalent CI/CD) preferred. - Hands-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. - Preferred: 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). - Experience with at least one major cloud platform (AWS, GCP, or Azure); Docker and basic containerised deployment preferred. - Comfortable working with very large, complex datasets residing in different data stores and formats. - Excellent verbal and written communication skills, with strong data visualisation ability and experience presenting to senior, non-technical stakeholders. - Demonstrated leadership potential and the presence to guide junior team members and represent the company with clients. - Nice to have: - Software engineering hygiene (preferred): typed Python (Pydantic), testing with pytest, packaging, and dependency management (uv). - Experience shipping LLM applications to production, including observability and cost/latency management (e.g., Langfuse, Phoenix, or similar LLMOps tooling). - Experience in the life sciences industry is preferred.   **KEY COMPETENCIES** - Executive Communication: Translates complex Data Science solutions into plain language for C-level and non-technical stakeholders. - Technical Depth: Brings rigorous statistical and modelling judgement, paired with fluency in modern GenAI/LLM approaches. - Discretion & Integrity: Handles sensitive client and internal information with professionalism and sound judgement. - Leadership & Charisma: Guides junior colleagues day to day, even without a formal management title, and takes pride in their growth. - Collaboration: A team player who builds strong working relationships across delivery teams, PMO, and clients.   **WHY YOU WILL LOVE IT HERE** - Work on real-world AI and advanced analytics solutions with measurable business impact. - Collaborate with a global team of engineers and data scientists. - Exposure to diverse industries, modern cloud platforms, and cutting-edge AI technologies. - A collaborative culture that values real outcomes. - High ownership, zero micromanagement. - Rapid learning opportunities and diverse challenges. - Flat organisational hierarchy with high visibility and accessibility to our leaders.