Primary Responsibilities -

Optimisation & AI/ML Modelling

- Translate complex business problems (resource allocation, scheduling, routing, treatment pathways, network optimisation) into mathematical optimisation and AI/ML solution approaches and scalable enterprise architectures.
- Design and develop advanced AI/ML and optimisation models including predictive, prescriptive, NLP, deep learning, and GenAI systems with a focus on novel algorithms and mathematical rigour.
- Formulate and solve complex optimisation problems using techniques such as:
- Linear and non-linear programming
- Integer and combinatorial optimisation
- Stochastic and robust optimisation
- Develop new modelling approaches combining ML and optimisation, including decision-focused learning, reinforcement learning, and constrained optimisation.
- Advance GenAI and optimisation integration, including retrieval optimisation, prompt optimisation, and constrained generation frameworks.
- Design scalable modelling frameworks that integrate optimisation solvers with ML/AI systems for real-world deployment.
- Explore and apply quantum and quantum-inspired optimisation methods (e.g., QAOA, annealing approaches, hybrid quantum-classical algorithms) where applicable to healthcare challenges.

Experimentation & Prototyping
- Conduct hands-on, hypothesis-driven experimentation including benchmarking optimisation approaches against ML baselines and evaluating trade-offs across accuracy, cost, and scalability.
- Design, build, and validate proof-of-concepts (POCs) and prototypes to assess technical feasibility, business value, scalability, and operational readiness.
- Develop production-oriented POCs that establish implementation patterns, reusable assets, architecture guidance, deployment approaches, and operational considerations required for enterprise adoption.
- Lead end-to-end model development lifecycle activities including problem formulation, data exploration, feature engineering, model selection, hyperparameter optimisation, validation, deployment, monitoring, and continuous improvement.

Production Enablement & Engineering Adoption
- Drive successful transition of validated POCs into production by partnering closely with engineering teams to ensure solutions are scalable, maintainable, secure, and aligned with enterprise architecture standards.
- Develop implementation-ready artefacts including reusable code components, optimisation pipelines, solver integration patterns, feature engineering frameworks, workflow templates, deployment recommendations, evaluation methodologies, and technical documentation to accelerate engineering adoption.
- Own the technical readiness of optimisation solutions by proactively identifying scalability constraints, solver performance limitations, operational dependencies, implementation risks, and mitigation strategies during experimentation.
- Apply MLOps best practices throughout experimentation and productionisation, including experiment tracking, model versioning, CI/CD integration, performance monitoring, and observability.

Research & Innovation




- Contribute to and explore frontier research in optimisation, operations research, and quantum computing, identifying applicability to healthcare use cases.
- Evaluate and recommend emerging AI and optimisation frameworks, platforms, and technology stacks, identifying opportunities for innovation and enterprise adoption.
- Publish and contribute to research artefacts including white papers, patents, and internal frameworks in AI, optimisation, and quantum applications.
- Support development and adoption of optimisation accelerators, reusable frameworks, and best practices across teams.

Responsible AI & Compliance
- Design and implement robust model evaluation frameworks including accuracy, solution quality, constraint satisfaction, drift detection, explainability, fairness, and business-specific performance metrics.
- Ensure Responsible AI and compliance, including explainability of optimisation decisions, fairness constraints, and regulatory alignment (HIPAA/PHI, SOC 2, HITRUST).
- Apply strong understanding of model explainability, responsible AI principles, fairness, bias mitigation, and AI governance frameworks throughout the solution lifecycle.

Stakeholder Engagement & Leadership
- Collaborate with business, product, architecture, and engineering teams to clarify requirements and align solutions with measurable business outcomes.
- Lead technical solution design discussions and provide AI and optimisation architecture recommendations that balance business objectives, solver performance, operational complexity, scalability, and compliance requirements.
- Communicate experimentation results, model performance, trade-offs, recommendations, implementation considerations, and business impact to technical and non-technical stakeholders.
- Mentor teams and drive AI and optimisation capability development across the organisation.
- Accelerate organisational AI and optimisation adoption by reducing the cycle time from experimentation to production deployment through repeatable patterns and reusable assets.

Measuring Success
- Quality and business impact of optimisation, ML, and GenAI POCs
- Production readiness of delivered solutions
- Percentage of POCs successfully adopted and deployed into production
- Adoption of reusable optimisation accelerators, workflows, models, and reference architectures
- Reduction in experimentation-to-production cycle time
- Delivery of measurable business outcomes enabled through productionised optimisation solutions

Comply with the terms and conditions of the employment contract, company policies and procedures, and any directives which may arise due to evolving business requirements.

Qualifications -

Required Qualifications

- Bachelor's degree in Computer Science, Mathematics,



Engineering, Operations Research, Applied Mathematics, or related field; MS/PhD strongly preferred.
- 10 years of experience in applied AI/ML or optimisation-focused roles, with strong ownership of enterprise-scale AI and optimisation initiatives.
- Proven experience translating business challenges into effective optimisation and AI/ML solution strategies and production-ready solution architectures.
- Robust foundation in:
- Mathematical optimisation, operations research, and statistics
- Machine learning, deep learning, and experimental design
- Hands-on experience with optimisation frameworks, including Pyomo, OR-Tools, Gurobi, and/or CPLEX.
- Hands-on experience with ML/DL frameworks: PyTorch and/or TensorFlow.
- Experience with combinatorial optimisation and large-scale decision systems.
- Demonstrated experience with hybrid ML and optimisation approaches.
- Familiarity with quantum computing concepts or quantum-inspired algorithms for optimisation.
- Strong programming skills in Python (NumPy, pandas, scientific computing) and SQL.
- Demonstrated experience designing, developing, validating, and delivering successful optimisation and AI proof-of-concepts that progressed into large-scale production environments.
- Familiarity with MLOps practices including experiment tracking, benchmarking, model versioning, and deployment automation.
- Proven ability to translate theoretical models into impactful, real-world solutions.
- Proven ability to collaborate effectively with engineering organisations to enable successful production adoption of AI and optimisation solutions.
- Strong analytical, problem-solving, communication, solution design, and stakeholder management skills.
- Proven ability to collaborate effectively across business, product, engineering, and leadership teams.

Preferred Qualifications -

- Experience with quantum frameworks such as Qiskit, Cirq, D-Wave, or Azure Quantum.
- Experience with reinforcement learning for decision optimisation.
- Experience with simulation-based optimisation and digital twins.
- Healthcare domain expertise including claims, EHR/HL7/FHIR, care pathways, resource planning, ICD/CPT coding, risk adjustment, quality measures, and de-identification.
- Publications or patents in optimisation, ML, or quantum computing; contributions to internal frameworks, accelerators, or enterprise AI innovation initiatives.
- Experience integrating optimisation into production systems in collaboration with engineering teams.
- Knowledge of Responsible AI in constrained decision-making systems.
- Experience building enterprise-scale Generative AI solutions integrated with optimisation pipelines.
- Big data platforms (Databricks, Snowflake, BigQuery) and streaming (Kafka); lakehouse patterns.
- MLOps stack: MLflow, SageMaker, Azure ML, or Vertex AI; model monitoring, observability, automated retraining, and deployment automation.
- Knowledge of security and compliance frameworks (SOC 2, HITRUST, HIPAA).
- Experience mentoring teams and driving AI capability development across organisations.

📌 Data Scientist (Bengaluru)
🏢 Optum
📍 Bengaluru