Data Scientist - Python/SQL (5-9 yrs)

Required Technical Skills :

1. Education : Holds post graduate degree in Computer Science, Statistics, Economics, Applied Mathematics, or other quantitative fields (BE, B. Tech, ME, M. Tech) from a reputed university

2. Programming : Strong proficiency in Python and SQL (mandatory); preference for candidates experienced in PySpark.

3. Statistical Methods : Proficient in advanced statistical techniques - regression, time series and optimization.

4. Machine Learning Libraries : Experience with scikit-learn, TensorFlow, Keras, or PyTorch and ML techniques - Random Forest, Gradient Boosting, SVM, Neural Network etc.

5. MLOps Tools : Familiar with tools such as Docker, Kubernetes, MLflow, or Kubeflow.

6. Data Visualization : Skilled in at least one data visualization tool (PowerBI, Tableau, or QlikView).

7. Cloud Platforms : Experience with at least one cloud platform (AWS, Azure, or GCP) for ML model development and deployment.

8. Model Explainability : Exposure to interpretability tools such as LIME or SHAP.

Consulting & Domain Skills :

1. Client Engagement : Proven experience in consulting, delivering custom ML solutions directly to clients.

2. Project Delivery : Experience in project management for data science projects and leading a technical team for the delivery of the project

3. Business Understanding : Ability to interpret client needs and transform them into actionable data science projects.

4. Communication : Excellent skills in presenting complex insights to technical and non-technical stakeholders.

5. Industry Knowledge : Strong business acumen with multi-industry experience, preferably in BFSI.

6. Presentation Skill : Must have skills to develop POV Documents, Technical Proposals, etc

Key Responsibilities :

1. Machine Learning Solution Design : Architect and build end-to-end machine learning solutions, covering data extraction, feature engineering, model development, validation, deployment, and monitoring, grounded in statistical analysis.

2. MLOps Implementation : Develop and maintain robust MLOps pipelines to automate and scale model deployment, ensuring workflow reliability.

3. Statistical Analysis and Validation : Apply statistical techniques such as hypothesis testing, A/B testing, and regression analysis to validate models, enhancing decision-making.

4. Data Science Expertise : Translate complex business problems into analytical solutions using statistical reasoning; provide expert guidance on data science use cases.

5. Client Collaboration : Engage directly with clients to gather requirements, develop ML strategies, and deliver tailored solutions aligned with their business goals.

6. Cross-Functional Collaboration : Work closely with data engineers, business analysts, and DevOps teams to facilitate seamless project execution.

7. Technical Documentation : Prepare technical proposals, proof of value (PoV) documents, and client presentations.