Lead Data Engineer

We are seeking a highly skilled Data Scientist to analyze complex datasets, build predictive models, and develop data driven solutions that enable business growth and operational efficiency. The ideal candidate should have strong expertise in statistics, machine learning, data analysis, and programming, with experience delivering end to end data science solutions in enterprise environments.

• Analyze structured and unstructured data to generate actionable business insights.
• Develop, train, and deploy machine learning and predictive analytics models.
• Perform data exploration, feature engineering, and statistical analysis.
• Build forecasting, classification, clustering, recommendation, and anomaly detection models.
• Design and execute experiments, A/B testing, and model evaluation frameworks.
• Develop data pipelines and automate analytical processes.
• Communicate findings through dashboards, visualizations, and presentations.
• Collaborate with business stakeholders, data engineers, and product teams to solve business problems.
• Monitor model performance and support continuous improvement initiatives.
• Ensure data quality, governance, and compliance with organizational standards.

• Strong proficiency in Python and SQL.
• Experience with machine learning libraries such as Scikit learn, TensorFlow, PyTorch, XGBoost, and Pandas.
• Expertise in statistical modeling, hypothesis testing, and predictive analytics.
• Experience with data wrangling, feature engineering, and exploratory data analysis.
• Knowledge of machine learning algorithms including Regression, Classification, Clustering, Time Series Forecasting, and NLP.
• Experience working with large datasets and cloud based data platforms.
• Strong understanding of data structures, algorithms, and model lifecycle management.
• Experience creating data visualizations using Power BI, Tableau, or Matplotlib.

• Experience with Generative AI, LLMs, NLP, and RAG architectures.
• Knowledge of MLOps, model deployment, and monitoring tools.
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Knowledge of Spark, Databricks, Snowflake, or other big data technologies.
• Familiarity with Docker, Git, and CI/CD pipelines.