Job
Description
Core Responsibilities 1. Project Leadership & Strategy Define, initiate, and drive AI/ML projects aligned to business goals. Lead project lifecycle: data ingestion, modeling, evaluation, deployment, and monitoring. Mentor and guide junior data scientists; review code, models, and analyses. Evangelize data science best practices and metrics across the organization. 2. Data Management & Feature Engineering Acquire, clean, and integrate large, complex datasets from multiple sources. Perform EDA, identify data trends, biases, and quality issues. Engineer robust features and reduce dimensionality for scalable modeling. 3. Modeling, Tuning & Validation Design and implement ML models (classical, ensemble, deep learning, NLP, CV) using Python, R, TensorFlow, PyTorch, or scikit-learn. Tune hyperparameters and optimize performance through techniques like cross-validation, A/B testing, and drift analysis. Ensure models adhere to fairness, ethics, privacy standards (e.g., GDPR, HIPAA). 4. Deployment & MLOps Collaborate with engineering to containerize and deploy models via APIs or microservices. Develop CI/CD pipelines, manage model versioning and monitor production performance and drift. 5. Visualization & Communication Build dashboards and visual reports to convey insights to non-technical stakeholders. Clearly communicate technical outcomes and business relevance in presentations and written reports. 6. R&D & Innovation Stay current with new AI/ML research, frameworks, and apply cutting-edge techniques. Experiment with novel methods (e.g., generative AI, transformers) and integrate improvements into production systems. Technical Skills & Tools Languages - Python, R, (Java/Scala for production) ML Frameworks - scikit-learn, TensorFlow, PyTorch, Keras Big Data & MLOps - Spark/Hadoop, Docker, Kubernetes, AWS SageMaker/Azure ML Databases - SQL, NoSQL, ETL pipelines Visualization BI - Tableau, Power BI, matplotlib, seaborn Version Control / CI - Git, MLflow, Jenkins (engineering collaboration) Soft Skills & Leadership Mentorship & Collaboration: Guides junior staff and collaborates with multi-disciplinary teams. Communication & Influence: Translates technical insights into actionable business decisions. Critical Thinking & Ethical Awareness: Applies domain knowledge and ethical responsibility to model development. Innovation & Learning Mindset: Pursues advanced methods and continuous improvement. Project Management: Balances project scope, milestones, and delivery timelines. Experience & Qualifications Education: Master s or PhD in Data Science, Computer Science, Statistics, or related fields. Experience: 5+ years in data science or ML, with a proven track record of delivering models into production. Domain Expertise: Preferred in finance, healthcare, marketing, or cybersecurity. Certifications: IAMML, TensorFlow Developer, AWS/Azure ML certifications are a plus.
