Key Responsibilities

📍Problem Formulation: Collaborate with product, engineering, and marketing teams to identify business challenges and translate them into well-defined data science projects. 📍Data Exploration & Preparation: Extract, clean, and preprocess large, complex datasets from various sources (e.g., SQL/NoSQL databases, APIs, logs). Perform rigorous exploratory data analysis (EDA) to uncover trends and patterns. 📍Modeling & Validation: 📍Develop, train, and validate predictive models using machine learning algorithms (e.g., regression, classification, clustering, time series forecasting). 📍Apply appropriate statistical methods for inference and hypothesis testing (e.g., t-tests, ANOVA, chi-squared (χ2) tests). 📍Implementation & Deployment: Work with Data and software Engineers to deploy models into production environments. Monitor model performance and conduct iterative improvements. 📍Experimentation: Design, execute, and analyze A/B tests to measure the impact of new features, algorithms, and business strategies. 📍Communication: Visualize and communicate complex findings, model results, and strategic recommendations to both technical and non-technical stakeholders in a clear and compelling manner.

The ideal employee must have:

📍Education: Bachelor’s or Master’s degree in a quantitative field like Computer Science, Statistics, Mathematics, Physics, Engineering, or a related discipline. 📍Experience: 3+ years of hands-on experience in a data science, machine learning, or quantitative analysis role. 📍Technical Skills: 📍High proficiency in Python and core data science libraries (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn). 📍Strong command of SQL for complex data querying and manipulation. 📍Solid theoretical and practical understanding of machine learning concepts and statistical modeling. 📍Hands-on experience with data warehousing solutions.

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