Senior Data Scientist - Machine Learning/Deep Learning

Description :

Key Responsibilities :

- Build, validate, and deploy Machine Learning (ML) and Deep Learning (DL) models (classification, regression, time series, NLP, recommendation, anomaly detection).

- Utilize AutoML platforms for rapid prototyping and benchmarking; transition successful candidates to production-grade pipelines.

- Design robust data modeling strategies (feature engineering, dimensionality reduction, model monitoring, drift detection).

- Apply statistical methods (hypothesis testing, experimental design, AB testing, Bayesian inference) and mathematical modeling (optimization, linear algebra, probability).

- Partner with data engineering to implement scalable pipelines (model versioning, CI/CD for ML, batch/real-time scoring).

- Conduct exploratory data analysis (EDA), build clear visualizations, and deliver actionable insights to stakeholders.

- Define evaluation metrics (e.g., AUC, F1, Precision/Recall, RMSE, MAP@K) and monitor model performance post-deployment.

- Document methodologies and deliverables; ensure reproducibility and governance (data privacy, compliance).

- Mentor junior data scientists; contribute to best practices, code reviews, and model lifecycle standards.

Required Skills & Experience :

- 7-13 years in data science/ML with end-to-end ownership from problem framing to production deployment.

- Strong proficiency in ML/DL : tree-based models, linear/logistic regression, neural networks (CNN/RNN/Transformers, as relevant), ensemble methods.

- Hands-on with AutoML (e.g., H2O.ai, DataRobot, Azure AutoML, Google Vertex AI, AutoKeras) for rapid prototyping.

- Programming : Python (preferred) or R; strong SQL for data extraction and transformations.

- Solid understanding of Statistics, Mathematics, and Data Modeling :

- Statistics : distributions, sampling, hypothesis testing, confidence intervals, regression diagnostics, AB testing.

- Mathematics : linear algebra, calculus (optimization), probability.

- Data Modeling : schema design, feature stores, dimensional modeling.

- Experience with model deployment and MLOps (Docker, APIs, model registries; CI/CD workflows; monitoring/observability).

- Experience with cloud platforms (Azure/AWS/GCP) and big data tools (Spark/Databricks, Hive/Presto) is highly desirable.

- Strong communication skills : ability to simplify complex analyses and influence decisions.