<p><p><b>Job Summary :</b></p><p><br/></p><p>We are looking for a highly skilled and results-driven Data Scientist to join our data and analytics team.
The ideal candidate will be responsible for designing and implementing advanced machine learning and AI models to solve real-world business problems.
You will work closely with cross-functional teams to gather data, build predictive and prescriptive models, and communicate insights that inform strategic decisions.
Your expertise will help drive innovation and data-driven transformation across the organization.<br/><br/><b>Key Responsibilities :</b></p><p><br/></p><p>- Collect, clean, and preprocess structured and unstructured data from diverse internal and external sources.<br/><br/>- Perform data wrangling, missing value imputation, feature engineering, and data transformation to prepare datasets for analysis and modeling.<br/><br/>- Work with large-scale datasets from relational databases, APIs, cloud sources, or raw files (e.g., CSV, JSON, logs, text).<br/><br/>Develop and deploy machine learning (ML) and AI models for :<br/><br/>- Classification, regression, clustering<br/><br/>- Recommendation systems<br/><br/>- Anomaly detection and forecasting (as needed)<br/><br/>- Perform model selection, hyperparameter tuning, cross-validation, and evaluation using best practices.<br/><br/>- Use AWS SageMaker, H2O.ai, or in-house tools to train, evaluate, and deploy models at scale.<br/><br/>- Package and deploy models as APIs or batch processes, integrating them into business workflows or applications<br/><br/>- Apply descriptive statistics, inferential statistics, and hypothesis testing to derive insights from data.<br/><br/>- Design experiments (e.g., A/B testing) and analyze their results.<br/><br/>- Translate business challenges into quantitative frameworks and model-driven solutions.<br/><br/>- Continuously track model performance (e.g., drift, degradation, real-world impact).<br/><br/>- Re-train, re-tune, or retire models as needed to maintain accuracy and relevance.<br/><br/>- Implement model versioning, logging, and audit trails for traceability.<br/><br/>- Work with stakeholders from product, marketing, engineering, and business teams to understand needs and define solutions.<br/><br/>- Present analytical results, data-driven recommendations, and visualizations to non-technical audiences.<br/><br/>- Document solutions, processes, assumptions, and results clearly and concisely.<br/><br/><b>Required Skills & Qualifications :</b></p><p><br/></p><p>- 3 - 6 years of experience in a Data Science or Machine Learning role.<br/><br/>Strong programming skills in Python, including libraries such as :<br/><br/>- NumPy, pandas, scikit-learn<br/><br/>- matplotlib, seaborn (for visualization)<br/><br/>- Solid command of SQL for querying, transforming, and analyzing data from relational databases.<br/><br/>- Experience deploying models using AWS SageMaker or similar cloud platforms.<br/><br/>- Familiarity with H2O.ai tools and automated machine learning (AutoML) frameworks.<br/><br/>- Strong understanding of statistical theory, data distributions, bias/variance trade-offs, and model evaluation metrics.<br/><br/>- Proficiency in the full ML lifecycle: from data exploration and feature engineering to deployment and monitoring.</p><br/></p> (ref:hirist.tech)