Key Responsibilities

• Lead design and development of end-to-end machine learning models.
• Own data science lifecycle: from exploration and experimentation to production deployment.
• Work with engineers and analysts to build pipelines and intelligent systems.
• Drive AI/ML use cases, including NLP, forecasting, anomaly detection, and optimization.
• Mentor junior team members and help establish best practices.
• Communicate findings and strategic recommendations to leadership.
• Assess, integrate, and extract data across multiple databases (structured and unstructured) to create usable datasets for analytics, dashboards, and business reporting.
• Design and implement automated data extraction and transformation workflows to support real-time reporting needs.
• Collaborate with IT and business units to understand data architecture and improve data accessibility and governance.

Required Skills & Experience

• 5+ years in data science or related field.
• Expert proficiency in Python (e.g., Scikit-learn, PyTorch/TensorFlow, Statsmodels).
• Strong SQL and experience with data engineering workflows.
• Proven ability to translate business problems into data science solutions.
• Hands-on experience with production ML pipelines and CI/CD.
• Familiarity with Azure cloud platforms.
• Excellent storytelling and stakeholder management skills.
• Proficiency in full-stack development (e.g., JavaScript/TypeScript, Node.js, React, or similar), enabling development of internal tools or dashboards where necessary.
• Strong understanding of relational and non-relational databases (e.g., SQL Server, MongoDB, PostgreSQL).
• Demonstrated experience in integrating backend services with frontend applications for end-to-end data solution

Additional Skills

• Experience with LLMs (e.g., OpenAI, Hugging Face Transformers).
• Background in building recommendation engines, predictive maintenance, or fraud detection.
• Familiarity with MLOps tools (e.g., MLflow, Kubeflow).
• Experience working with unstructured data (text, image, audio).
• “Experience developing and maintaining business intelligence dashboards (e.g., Power BI, Tableau, custom-built).”
• “Knowledge of DevOps tools and practices for managing infrastructure supporting analytics workloads.”