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Senior / Lead Data Scientist - MLOPS

Senior / Lead Data Scientist - MLOPS

Material+

5 - 10 years

13 - 18 Lacs

gurugram

Posted:Just now| Platform:

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Skills Required

automation simulation networking gcp packaging reliability testing scheduling forecasting monitoring python

Work Mode

Work from Office

Job Type

Full Time

Job Description

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.

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Senior / Lead Data Scientist - MLOPS