Data Scientist

Must Have

• 5 to 8 years of hands-on experience in Data Science, Machine Learning, Predictive Analytics
• Strong proficiency in Python data manipulation, statistical analysis, ML, and model development using Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch and related frameworks
• Solid understanding of statistics, probability theory, hypothesis testing, regression analysis, sampling techniques, and experimental design for data-driven decision making
• Ability to apply statistical/ML techniques to analyze large datasets, identify patterns, build predictive models, and generate actionable business insights
• Experience building REST APIs and integrating AI models into production environments
• Strong understanding of ML algorithms, forecasting techniques, and optimization methods
• Excellent analytical, problem-solving, and communication skills; able to work cross-functionally

Good to Have

• Experience with Generative AI / Large Language Models (LLMs)
• Experience with SQL and NoSQL databases, data warehouses, and big data technologies
• Familiarity with cloud platforms (AWS) and containerization (Docker, Kubernetes)

Roles & Responsibilities

• Collaborate with business stakeholders, product managers, and engineering teams to translate business requirements into analytical solutions
• Extract and integrate data from multiple sources via APIs, databases, and file-based interfaces
• Analyze structured/unstructured data to identify trends and actionable insights
• Perform data preprocessing, feature engineering, model development, deployment, and evaluation
• Evaluate, fine-tune, and optimize AI/ML models for accuracy, performance, scalability, and reliability
• Develop APIs/services for model deployment and integration with enterprise systems
• Ensure data quality, governance, security, and compliance across the analytics lifecycle
• Stay current with emerging AI/GenAI/ML/data science tech and recommend adoption where relevant