Lead Software Engineer

Data Exploration & Analysis: Collect, clean, and analyze structured and unstructured data from multiple sources to uncover meaningful insights and trends. Model Development: Design, build, and deploy machine learning and statistical models to solve business problems such as forecasting, classification, recommendation, and optimization. Feature Engineering: Identify, create, and select the most relevant variables and features to improve model performance and interpretability. Experimentation : Apply hypothesis testing, A/B testing, and cross-validation techniques to evaluate model robustness and performance. Production Deployment: Work with data engineering and MLOps teams to operationalize models, monitor performance, and ensure scalability and reliability in production environments. Visualization & Storytelling: Communicate complex analytical findings in clear, concise,and visually compelling ways for both technical and non-technical audiences. Collaboration: Partner with business teams to understand objectives, define success metrics, and translate business requirements into analytical frameworks. incorporating them into projects and best practices.

Required Skills: Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Data Science,Engineering, or related fields. Ph.D. preferred but not mandatory. Experience: 7–10 years of experience in data science, advanced analytics, or applied machine learning roles. Technical Expertise: Strong proficiency in Python (NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow) orR.Expertise in machine learning algorithms (supervised, unsupervised, NLP, and deep learning).Strong understanding of statistical modeling, probability, and mathematical optimization. Experience with SQL and data manipulation in large datasets. Familiarity with big data platforms (e.g., Spark, Databricks, Hadoop) and cloud environments (AWS, Azure, or GCP). Exposure to MLOps tools (MLflow, Kubeflow, Airflow, Docker, CI/CD).Experience with data visualization tools (Power BI, Tableau, Matplotlib, Seaborn, Plotly).

Preferred Skills Experience with NLP, computer vision, or time-series forecasting. Familiarity with data warehousing and ETL/ELT concepts(e.g., Snowflake, Redshift, BigQuery).Exposure to deep learning frameworks such as TensorFlow, PyTorch, or Keras. Knowledge of model governance, data ethics, and responsible AI principles. Experience leading or mentoring junior data scientists or analysts.