Senior/Lead Data Scientist - MLOPS

About US:-

We turn customer challenges into growth opportunities.

Material is a global strategy partner to the world’s most recognizable brands and innovative companies. Our people around the globe thrive by helping organizations design and deliver rewarding customer experiences.

We use deep human insights, design innovation and data to create experiences powered by modern technology. Our approaches speed engagement and growth for the companies we work with and transform relationships between businesses and the people they serve.

Srijan, a Material company, is a renowned global digital engineering firm with a reputation for solving complex technology problems using their deep technology expertise and leveraging strategic partnerships with top-tier technology partners. Be a part of an Awesome Tribe

Job Title: Senior/Lead ML Engineer / Data Scientist (Regression & MLOps)

Experience: 5+ years
Employment Type: Full-time

About the Role

We’re looking for a hands-on Senior/Lead ML Engineer / Data Scientist with a strong foundation in supervised learning (especially regression) and mathematical optimization, who can build, deploy, and sustain production ML solutions. You will own models end-to-end—from problem framing and feature engineering to containerized deployment (Docker/Kubernetes), MLOps automation, and production monitoring in a cloud environment (Azure/AWS/GCP).

What You’ll Do

• Solution Building
• • Frame business problems as regression/forecasting tasks; design robust baselines and iterate to production-grade models.
  • Engineer features, select algorithms (e.g., Linear/GLM, Tree-based methods, GBMs), and run disciplined experimentation and hyper-parameter tuning.
  • Apply optimization techniques (LP/MIP/heuristics/simulation) to turn predictions into decisions (pricing, allocation, scheduling, routing, etc.).
• Deploying & Sustaining
• • Package models as services (Docker), orchestrate on Kubernetes (or Azure ML endpoints/SageMaker/GCP Vertex), and implement CI/CD for ML.
  • Own MLOps: reproducible training, model registry, automated evaluation, canary/blue-green releases, data & concept drift monitoring, retraining triggers.
  • Build observability: metrics, tracing, and alerting (e.g., Prometheus/Grafana/Evidently).
• Collaboration & Ownership
• • Partner with product, data, and engineering to translate goals into measurable outcomes and SLAs.
  • Communicate trade-offs clearly; document assumptions, data contracts, and runbooks.
  • Demonstrate strong ownership: drive delivery timelines, unblock dependencies, and maintain production stability.

Required Skills & Experience

• Core ML: 5 years hands-on with supervised learning, with deep experience in regression (tabular data, time-based features, leakage control, calibration, error analysis).
• Optimization: Practical experience with LP/MILP/CP or heuristic approaches (e.g., PuLP/OR-Tools/Pyomo) to operationalize decisions.
• Python & Ecosystem: Proficient with pandas, NumPy, scikit-learn, XGBoost/LightGBM; comfortable with PyTorch/TensorFlow for custom components if needed.
• MLOps: Model packaging, MLflow (or equivalent) for tracking/registry, data versioning (e.g., DVC/LakeFS), and pipeline orchestration (Airflow/Kubeflow).
• DevOps/Platform: Docker, Kubernetes, Git, CI/CD (GitHub Actions/GitLab CI/Azure DevOps), artifact registries; environment management (poetry/conda).
• Cloud: Experience deploying on Azure/AWS/GCP (managed training/inference, storage, IAM, networking basics).
• Quality & Reliability: Testing for data/feature integrity, unit/integration tests, performance profiling, cost/perf optimization.
• Soft Skills: Clear communication, structured problem-solving, stakeholder management, and ownership mindset.

What We Offer

• Professional Development and Mentorship.
• Hybrid work mode with remote friendly workplace. (6 times in a row Great Place To Work Certified).
• Health and Family Insurance.
• 40+ Leaves per year along with maternity & paternity leaves.
• Wellness, meditation and Counselling sessions.