Operations Research & Data Science Analyst

Greeting from Teknikoz,

Experience: 2-3 years

Location: Bangalore

Role Summary

The Consultant / Analyst – Operations Research & Data Science will design and implement advanced analytics and optimization solutions to address complex supply‑chain and business problems. You will apply mathematical modeling, operations research, and data science techniques to improve efficiency, reduce costs, and support strategic decision‑making across inventory, production, logistics, and distribution. The role requires close collaboration with cross‑functional teams to deliver actionable, data‑driven insights.

Key Responsibilities

• Operations Research & Optimization
• Develop and validate mathematical and optimization models for supply‑chain systems, including:
• Inventory optimization
• Production planning
• Logistics and distribution networks
• Apply operations research techniques such as:
  
• Linear and integer programming
• Network flow models
• Simulation and heuristic algorithms
• Translate business requirements, constraints, and objectives into formal analytical and optimization models.

Data Analysis & Insights

• Analyze large datasets to identify trends, patterns, and decision‑support insights using statistical and data‑science methods.
• Ensure data quality and model accuracy through collaboration with data science, engineering, and business teams.
• Communicate findings to non‑technical stakeholders via dashboards, visualizations, and written reports.
• Technical Implementation & Integration
• Develop APIs and microservices to integrate optimization and analytics solutions into business applications and platforms.
• Follow best practices for code version control, testing, and technical documentation (e.g., Git, code reviews, inline comments, architecture docs).
• Stay current with emerging trends in operations research, optimization, machine learning, and supply‑chain analytics.

Required Qualifications

Technical Skills:

Strong expertise in optimization and mathematical modeling techniques.

Advanced Python programming skills, including optimization and data‑analysis libraries (e.g., PuLP, CVXPY, SciPy, pandas, NumPy).

Experience with SQL, statistical analysis, and data visualization tools (e.g., matplotlib, seaborn, Power BI, Tableau).

Strong object‑oriented programming (OOP) skills for building maintainable, scalable analytical systems.

Domain Knowledge:

Solid understanding of supply‑chain concepts such as demand forecasting, inventory management, production planning, and logistics.

Preferred Qualifications

• Supply‑chain certifications (e.g., CPIM, CSCP or equivalent).
• Cloud or platform‑related certifications (e.g., AWS, Azure, GCP, or analytics platforms).
• Training or certification in Machine Learning or Advanced Analytics (e.g., Coursera, edX, Google, Microsoft, or similar).