Job Description

Job Summary

Job Title: Data Scientist (AI/ML) – Data Engineering & MLOps

Role Summary

We are seeking a hands-on Data Scientist who can operate across data engineering and machine learning to deliver production-ready analytics/AI solutions. This role includes building data pipelines and curated datasets, developing and evaluating ML models, and supporting model operationalization with MLOps practices across the ML lifecycle (from data discovery through deployment and monitoring).

Key Responsibilities

• Partner with business and technical stakeholders to translate problems into data-driven and ML solutions, communicating insights and model outcomes clearly
• Perform exploratory data analysis, feature engineering, and build predictive/advanced analytics models using Python-based DS libraries.
• Design, build, and maintain data pipelines and datasets to support analytics and model training (batch/near-real-time as needed).
• Apply MLOps discipline across the ML lifecycle: data validation, packaging, deployment support, and performance monitoring concepts.
• Collaborate in Agile delivery (sprint goals, user stories, iteration based on feedback).

Required Skills & Experience

• Strong hands-on Python for data analysis and ML model development.
• Experience with data engineering fundamentals: data integration, data processing, and pipeline/orchestration concepts.
• Solid grounding in ML lifecycle / MLOps concepts (validation, versioning, deployment, monitoring).
• Ability to assess data sources for quality, relevance, and usability; translate business rules into analytics logic.
• Strong communication skills—able to bridge business stakeholders and technical teams.

Preferred / Nice to Have

• Cloud exposure (AWS/Azure) and understanding of operationalizing ML solutions (CI/CD, monitoring patterns).
• Experience with modern AI/GenAI patterns (e.g., RAG/LLMs/knowledge graphs) if project requires it.
• Experience building reliable production-grade AI pipelines and data quality monitoring.

Key Responsibilities

1. Design And Implement Advanced Machine Learning Algorithms Using Tensorflow And Pytorch To Extract Insights From Large Datasets, Delivering Customized Analytical Reports Tailored To The Needs Of Colleagues, Clients, And Stakeholders.
2. Leverage Python And Sql To Develop And Optimize Data Models That Address Specific Organizational Challenges, Ensuring Effective Integration And Management Of Data.
3. Analyze And Mine Large Datasets To Identify Trends And Patterns, Utilizing Advanced Analytics Techniques To Interpret Findings And Provide Actionable Recommendations Based On Experimental Results.
4. Collaborate With Cross-Functional Teams To Identify Opportunities For Utilizing Data Insights To Formulate Strategic Business Solutions That Enhance Operational Efficiency And Product Offerings.
5. Create Comprehensive Visualizations And Reports Using Data Visualization Tools To Communicate Complex Analysis And Results Clearly, Enabling Informed Decision-Making For Customers And Stakeholders.

Skill Requirements

1. - Strong Knowledge Of Machine Learning Principles And Algorithms Using Tensorflow And Pytorch.
2. - Proficient In Programming Languages Such As Python For Data Analysis And Model Development.
3. - Solid Understanding Of Sql For Data Manipulation And Querying Large Databases.
4. - In-Depth Experience With Data Analytics Techniques And Tools For Interpreting Complex Datasets.
5. - Excellent Collaboration And Communication Skills To Work With Diverse Teams And Stakeholders.

Other Requirements

1. Optional But Valuable Certifications: Certified Data Scientist (Cds), Tensorflow Developer Certificate, Or Professional Data Engineer Certification.