Description :

About the Role :

We are looking for an innovative Data Scientist specializing in Machine Learning (ML) to join our central Data Science team.

This role is crucial for developing and deploying predictive and prescriptive models that drive core business decisions, from customer segmentation to forecasting and process optimization.

You will be responsible for the entire ML lifecycle, from initial data exploration to production deployment (MLOps).

Key Responsibilities :

- Model Development : Design, develop, and implement scalable Machine Learning, Deep Learning, and statistical models (Supervised/Unsupervised Learning, Time Series, NLP/Computer Vision) to solve complex business problems.

- Data Engineering for ML : Collaborate with Data Engineers on data collection, cleaning, feature engineering, and validation using large-scale data processing tools (PySpark/Databricks).

- MLOps & Deployment : Work with ML Engineers to productionize models using containerization (Docker) and orchestrators (Kubernetes/Kubeflow).

- Utilize experiment tracking tools like MLflow or DVC.

- Research & Experimentation : Conduct rigorous analytical experiments, A/B testing, and statistical analysis to validate model performance and business impact.

- Stay current with cutting-edge ML research and frameworks.

- Communication : Clearly articulate complex technical results and model insights to both technical teams and executive stakeholders through presentations and visualizations (Matplotlib, Seaborn, Tableau/Power BI).

- Code Best Practices : Write highly optimized, production-grade code in Python, adhering to software engineering best practices.

Required Technical Skills :

- Programming : Expert proficiency in Python (Pandas, NumPy, Scikit-learn, SciPy) and strong command of SQL.

- Machine Learning Frameworks : Deep practical experience with at least one major deep learning framework (TensorFlow or PyTorch).

- Statistical/Math Foundation : Excellent knowledge of statistical modeling, probability, linear algebra, and optimization techniques.

- Big Data/Cloud : Experience working with big data technologies (Spark/PySpark) and utilizing cloud ML services (AWS Sagemaker, Azure ML, or GCP AI Platform).

- MLOps : Hands-on experience with the ML deployment lifecycle, including model serving, monitoring, and pipeline automation.

- Tools : Git (Version Control), Docker, MLflow/DVC.

Specialization (Preferred) :

- Experience in a specialized area like Natural Language Processing (NLP), Generative AI (LLMs), or Computer Vision