Data Scientist-FDE

SENIOR SOFTWARE ENGINEER (with Data Science) – Forward Deployed

Location: Chennai | Type: Full-time

Your Mission

Software Engineer to design, build, and maintain full-stack systems delivering business value. Work across backend, frontend, cloud, and AI—developing RAG/LLM solutions, using data insights, and ensuring high-quality, reliable code delivery.

What You'll Do

Responsibilities

Business: Apply domain knowledge of commercial operations to technical solutions. Bridge business and technology conversations fluently, speaking the domain language naturally. Shadow operations to build understanding and make better technical decisions by understanding broader business context.

Delivery: Deliver working solutions rapidly — days not weeks. Use prototypes to build stakeholder trust, know when to stop prototyping and start productionising, and balance speed with appropriate quality. Deliver complete features end-to-end independently across frontend, backend, database, and infrastructure.

Generative AI: Design production RAG systems with appropriate chunking, embedding, and retrieval strategies. Optimise for relevance and latency, handle edge cases, and evaluate end-to-end system quality. Design evaluation frameworks with custom evaluators tailored to your use case. Build golden datasets and run experiments to compare prompt and model changes systematically.

Data Science & Analytics: Perform exploratory data analysis to inform solution design and validate assumptions. Apply statistical methods to understand relationships in operational data and build predictive models where they add business value. Evaluate model performance, communicate analytical findings to non-technical stakeholders, and integrate data-driven insights into software solutions.

Documentation: Create comprehensive documentation for complex systems. Write precise specifications that enable accurate AI-generated code, establish documentation practices for your projects, and ensure docs are discoverable. Identify patterns across implementations and propose candidates for generalisation.

Role Behaviours

Own the Outcome: Take end-to-end ownership of features and business outcomes. Accept technical debt intentionally when it accelerates value delivery. Build trust through rapid delivery of working solutions. Own stakeholder relationships and balance quality with delivery speed. AI may generate the code, but responsibility for outcomes remains with you.

Be Polymath Oriented: Bridge gaps between engineering, design, business, data science, and the pharmaceutical domain. Rapidly immerse in new domains. Speak the language of Commercial operations and make better decisions by understanding the broader business context. See connections across disciplines that others miss.

Communicate with Precision: Separate requirements, designs, and tasks with precision. Enable AI to generate accurate code through clear specifications. Translate between technical and business language fluently. Facilitate productive discussions and reduce ambiguity in everything you communicate.

Working-level Skills

Full-Stack Development: You deliver complete features end-to-end independently — frontend, backend, database, and infrastructure. You make pragmatic technology choices and deploy what you build.

Architecture & Design: You design components and services independently for moderateto-high complexity. You make appropriate trade-off decisions, document design rationale, and consider AI integration points in your designs.

Code Quality & Review: You produce consistently high-quality, well-tested code. You review AI-generated code critically and never ship code you don't fully understand. You identify edge cases and ensure adequate test coverage.

Problem Discovery: You navigate ambiguous problem spaces independently. You discover requirements through observation and user shadowing, reframe problems to find highervalue solutions, and distinguish symptoms from root causes.

Rapid Prototyping & Validation: You deliver working solutions rapidly (days not weeks). You use prototypes to build stakeholder trust, know when to stop prototyping and start productionising, and balance speed with appropriate quality.

Retrieval Augmentation: You design production RAG systems with appropriate chunking, embedding, and retrieval strategies. You optimise for relevance and latency, handle edge cases, and evaluate end-to-end system quality.

AI-Augmented Development: You integrate AI tools strategically into your development workflow. You review AI-generated code with the same rigour as human code and never ship code you don't fully understand.

Multi-Audience Communication: You present complex topics clearly to any audience, facilitate productive discussions, translate between technical and business language fluidly, and write compelling proposals and specifications.

Business Immersion: You apply deep domain knowledge to technical solutions, bridge business and technology conversations fluently, speak the domain language naturally, and shadow operations to build understanding.

Foundational-level Skills

Stakeholder Management: You proactively update stakeholders on progress, handle basic expectation setting, and escalate concerns appropriately. You build rapport with regular collaborators and manage expectations around delivery timelines.

DevOps & CI/CD: You configure basic CI/CD pipelines, understand containerisation, and can troubleshoot common build and deployment failures.

Cloud Platforms: You deploy applications to cloud platforms and use common services (compute, storage, databases, queues). You understand cloud pricing and basic security configuration.

AI Evaluation & Observability: You instrument applications with tracing to capture execution flow. You create evaluation datasets from production data, run basic LLM-asjudge evaluations, and apply pre-built evaluators for common metrics like faithfulness and relevance.

Data Integration: You create simple data transformations and handle common data formats. You identify and report data quality issues and understand basic ETL concepts.

Data Analysis: You perform exploratory data analysis independently, create effective visualisations, and identify patterns in data. You ask good questions about data quality and context.

Statistical Modeling: You apply common statistical tests appropriately and interpret pvalues, confidence intervals, and effect sizes. You recognise when assumptions are violated.

Model Development: You implement standard ML pipelines (data prep, training, evaluation), evaluate model performance appropriately, and avoid common pitfalls like data leakage.

Awareness-level Skills

Site Reliability Engineering: You understand SLIs, SLOs, and error budgets conceptually. You can use monitoring dashboards and escalate issues appropriately.

Data Modeling: You understand the difference between relational and non-relational data stores. You can create basic schemas from specifications with guidance.

AI Literacy: You understand basic AI concepts (training, inference, prompts) and can recognise AI-powered features in products. You know AI has limitations and when traditional approaches may be better.

Model Fine-Tuning: You understand fine-tuning concepts (transfer learning, domain adaptation) and when fine-tuning is appropriate versus using prompting or RAG. You can use fine-tuning APIs with guidance.

Synthetic Data Generation: You understand what synthetic data is and why it's used (privacy, availability, testing). You can use pre-generated synthetic datasets and recognise the difference between synthetic and real data.

What You Bring

• Bachelor's degree in Computer Science, Software Engineering, Statistics, or related field with 7-10 years of relevant professional experience.
• Strong production experience with Python and JavaScript/TypeScript across backend and frontend.
• Hands-on experience with modern frontend frameworks (such as Next.js or React) and backend API development.
• Production experience with cloud platforms (AWS preferred; Azure or GCP also valued), including infrastructure-as-code tools (such as CloudFormation or Terraform).
• Working knowledge of multiple database paradigms, including relational databases (such as PostgreSQL), document databases, and key-value stores (such as Redis).
• Experience with CI/CD pipelines (such as GitHub Actions) and a strong understanding of path-to-production practices.
• Demonstrable fluency with AI coding tools (such as Claude Code, Cursor, GitHub Copilot, or similar) and experience building agentic engineering workflows.
• Hands-on experience building production generative AI applications (LLM integrations, vector databases, RAG systems) is essential.
• Experience with data analysis, statistical modeling, or machine learning in a professional context is required. This could include exploratory data analysis, building regression or classification models, A/B test design, or integrating ML predictions into software products.
• Familiarity with data science tooling (such as pandas, scikit-learn, or equivalent) and comfort working in Jupyter notebooks or similar environments.
• Experience navigating ambiguous problem spaces, working directly with business stakeholders and end users, and shipping working solutions rapidly is strongly valued.
• Experience in an embedded, forward-deployed, or consulting-style engineering model is a strong plus.

Ready to Apply

Let's build the future of health together. Apply below or reach out to: [Confidential Information]