Lynx Analytics

Senior Data Scientist (US)

New York, United StatesPosted 208d ago

Senior

About the role

<p><strong><u>ROLE SUMMARY</u></strong></p>

<div class="ql-block" data-block-id="block-4f342aeb-71ef-406b-8b3a-7cef4a652d02">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.</div>

<div class="ql-block" data-block-id="block-yOwb8pxlRq"><strong><u>KEY RESPONSIBILITIES</u></strong></div>

<p><strong>Solution Design & Delivery</strong></p>

</div>

<ul>

<li>Design and deliver end-to-end solutions for defined data science problems, combining classical modelling, data transformation, and Generative AI / LLM techniques.</li>

<li>Work hands-on with very large datasets across disparate stores and formats, from ingestion and transformation through to modelling and validation.</li>

<li>Apply statistical and machine learning methods to business problems such as customer retention, campaign management, and commercial performance optimisation.</li>

</ul>

<div class="ql-block" data-block-id="block-Lg-VZG1Xl7"><strong>Client Communication & Leadership</strong></div>

<ul>

<li>Present results and prepare client-ready materials for project stakeholders, including C-level audiences, translating technical work into clear business narratives.</li>

<li>Lead smaller data science workstreams, with support from internal leadership and the PMO, including day-to-day guidance for junior team members.</li>

<li>Partner with delivery and account teams to scope problems, set realistic timelines, and manage stakeholder expectations.</li>

</ul>

<div class="ql-block" data-block-id="block-ibVK98yX-j"><strong>Knowledge Building</strong></div>

<ul>

<li>Create reusable documentation, presentations, and code libraries during projects so future engagements can build on prior work.</li>

<li>Participate in internal education, research, and knowledge-sharing initiatives that raise the technical bar across the practice.</li>

</ul>

<div class="ql-block" data-block-id="block-Nnx4v_dxls"><strong><u>SKILLS, QUALIFICATIONS AND EXPERIENCE</u></strong></div>

<ul>

<li>8+ years of overall experience in data science, with a track record of leading analytical workstreams independently.</li>

<li>Degree in Mathematics, Statistics, Economics, Computer Science, Engineering, or a related field; MSc or PhD preferred.</li>

<li>Solid grounding in probability theory, statistics, and core data science algorithms, with applied experience in areas such as customer retention and campaign management.</li>

<li>Strong hands-on proficiency in Python for data analysis, modelling <strong>(PyTorch, TensorFlow, or JAX)</strong>, and productionising code.</li>

<li>Strong SQL, and comfort working across common data stores (relational, columnar/warehouse, and vector databases).</li>

<li>Git and GitHub proficiency, including branching workflows and code review; experience with GitHub Actions (or equivalent CI/CD) preferred.</li>

<li>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.</li>

<li>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).</li>

<li>Experience with at least one major cloud platform (AWS, GCP, or Azure); Docker and basic containerised deployment preferred.</li>

<li>Comfortable working with very large, complex datasets residing in different data stores and formats.</li>

<li>Excellent verbal and written communication skills, with strong data visualisation ability and experience presenting to senior, non-technical stakeholders.</li>

<li>Demonstrated leadership potential and the presence to guide junior team members and represent the company with clients.</li>

<li>Nice to have:

<ul>

<li>Software engineering hygiene (preferred): typed Python (Pydantic), testing with pytest, packaging, and dependency management (uv).</li>

<li>Experience shipping LLM applications to production, including observability and cost/latency management (e.g., Langfuse, Phoenix, or similar LLMOps tooling).</li>

<li>Experience in the life sciences industry is preferred.</li>

</ul>

</li>

</ul>

<div class="ql-block" data-block-id="block-dL1_H1G4ch"><strong><u>KEY COMPETENCIES</u></strong></div>

<ul>

<li>Executive Communication: Translates complex Data Science solutions into plain language for C-level and non-technical stakeholders.</li>

<li>Technical Depth: Brings rigorous statistical and modelling judgement, paired with fluency in modern GenAI/LLM approaches.</li>

<li>Discretion & Integrity: Handles sensitive client and internal information with professionalism and sound judgement.</li>

<li>Leadership & Charisma: Guides junior colleagues day to day, even without a formal management title, and takes pride in their growth.</li>

<li>Collaboration: A team player who builds strong working relationships across delivery teams, PMO, and clients.</li>

</ul>

<ul>

<li>Work on real-world AI and advanced analytics solutions with measurable business impact.</li>

<li>Collaborate with a global team of engineers and data scientists.</li>

<li>Exposure to diverse industries, modern cloud platforms, and cutting-edge AI technologies.</li>

<li>A collaborative culture that values real outcomes.</li>

<li>High ownership, zero micromanagement.</li>

<li>Rapid learning opportunities and diverse challenges.</li>

<li>Flat organisational hierarchy with high visibility and accessibility to our leaders.</li>

</ul>