Data Scientist ML

Jobworld India

Job Title : Senior Data Scientist - Machine Learning

Job Purpose :

The role is responsible for designing, developing, and productionizing AI/ML models that deliver measurable business impact. It owns the full lifecycle from framing business problems and shaping datasets to model training, deployment with MLOps best practices, and post- launch monitoring. The role ensures models are accurate, reliable, and aligned with business objectives, while translating complex business requirements into actionable AI solutions and promoting transparency across key processes and performance indicators.

Core Activities Experience:

Proven experience applying AI/ML solutions in real-world domains such as aviation, transportation, or enterprise operations. Strong track record in translating business challenges into AI/ML opportunities with measurable business impact.

Expertise in deep learning and statistical methods, with hands-on model development and production deployment. Familiarity with Responsible AI principles and their application in regulated industries.

Skills, Tools, and Systems Experience:

• Programming & Data Science: Python, R, SQL, StreamLit
• ML & AI Frameworks: TensorFlow, PyTorch, Keras, scikit-learn, Hugging Face
• MLOps & Deployment: Docker, Kubernetes, Git CI/CD
• Data Engineering: Apache Spark, Kafka, dbt
• Cloud Platforms: Azure ML
• Visualization: Power BI, Tableau
• Responsible AI Tools: AI Governance Platform (e.g. Atlan)

Machine Learning Model Development & Deployment (30%)

• Design, train, validate, and deploy advanced ML/DL models (NLP, CV, transformers, statistical learning).
• Optimize models for accuracy, scalability, and latency to ensure production readiness.
• Apply Responsible AI principles, including fairness, bias detection, and explainability.

Data Engineering & MLOps (20%)

• Build and manage pipelines, feature stores, and ingestion frameworks (real-time and batch).
• Implement MLOps practices: CI/CD workflows, registries, automated retraining, and observability.
• Monitor and resolve issues related to data drift, leakage, and model reliability.

Governance, Compliance & Responsible AI (30%)

• Ensure compliance with GDPR, ISO standards, ICAO digital/AI frameworks, and organizational policies.
• Promote transparency of AI-driven KPIs and processes across the enterprise.
• Conduct fairness audits, model explainability, and ethical risk assessments.

Problem Framing & Business Alignment (10%)

• Translate complex, ambiguous business challenges into AI/ML opportunities.
• Define scope, feasibility, and expected value of AI initiatives.
• Advise stakeholders on risks, trade-offs, and adoption strategies.

Stakeholder Engagement & Decision Support (10%)

• Deliver insights, dashboards, and validation reports to senior management and cross-functional teams.
• Influence adoption of AI solutions using expert authority and evidence-based recommendations.
• Act as a subject matter expert, supporting enterprise-wide AI strategy and operational decisions.