Data Science Architect (Standard)

Key Responsibilities

1. Business Consulting, Problem Formulation & Proposal Development

• Engage with business teams and leadership to clarify, shape, and structure fuzzy business problems into clear analytical frameworks.

• Develop compelling proposals, problem statements, and solution blueprints—highlighting differentiated approaches, methodologies, and business impact.

• Quantify expected value, define success metrics, and build MVP roadmaps that demonstrate rapid value realization.

• Bring strong pre-sales thinking to help win new internal or external analytical projects.

2. Solutioning & Technical Delivery

• Lead end-to-end development of analytical solutions using:

  • Regression, classification, clustering, segmentation

  • Forecasting and time-series modeling (ARIMA/SARIMA/ETS/Prophet)

  • Optimization models & statistical inference

  • Experimentation, uplift modelling, causal inference (preferred)

• Own solution architecture: data validation ? feature engineering ? modeling ? evaluation ? deployment-ready output.

• Bring technical differentiation—ability to decide when classical ML, statistical modelling, optimization, heuristics, or applied AI/LLMs are appropriate.

• Manage delivery from concept to MVP, ensuring rigor, speed, and business alignment.

3. Stakeholder Engagement & Business Impact

• Work with cross-functional partners (Product, Engineering, Business, CXOs) and drive trusted advisor-style engagement.

• Present insights with a compelling narrative: clear, concise, business-friendly.

• Influence business strategy by identifying opportunities, risks, and quantifiable value.

• Bridge the gap between technical capability and business outcomes.

• Strong communicator and powerpoint writing skills

4. Leadership & Talent Development

• Lead and mentor a high-performing team of data scientists and analysts.

• Enforce standards in methodology, experimentation, code quality, and documentation.

• Review work products for statistical rigor and business relevance.

• Foster a culture of curiosity, excellence, and clear thinking.

5. Governance, Standards & Best Practices

• Define and enforce processes for documentation, reproducibility, model governance, and versioning.

• Partner with Data Engineering to ensure high-quality data pipelines and scalable architecture.

• Drive high standards in modelling practices, experimentation design, and analytical storytelling.

• Contribute to innovative methods/approaches

Required Skills & Qualifications

Technical Skills

• Deep expertise in classical ML:

  • Regression (linear/logistic/regularized)

  • Decision Trees, Random Forest, Gradient Boosting

  • Clustering (K-means, hierarchical, density-based)

  • Forecasting (ARIMA/SARIMA/ETS, Prophet)

  • Optimization & statistical inference

  • Hypothesis testing & experimental design

• Strong proficiency in Python or R (pandas, NumPy, SciPy, scikit-learn, statsmodels, etc.)

• Strong SQL skills

• Good understanding of data pipelines, ETL concepts, and cloud environments (GCP/AWS/Azure)

• Interested in AI/Gen AI based approaches

• Experience with Power BI/Tableau for business-focused insight delivery

Consulting & Analytical Thinking

• Ability to translate abstract business questions into structured analytical frameworks.

• Experience crafting value-based proposals, solution architectures, and MVP plans.

• Excellent data storytelling and narrative development.

• Comfortable with large datasets and deep exploratory analysis.

• Curious learner and willing to adapt to new tools/approaches

Leadership & Project Management

• Team management experience.

• Strong project management: scoping, planning, prioritization, and delivery.

• Ability to guide solution design, review artifacts, and ensure high-quality outcomes.

• Strong stakeholder management and communication skills.

• Can manage conflict and solve issues

Preferred Qualifications

• Master’s degree in Statistics, Mathematics, Analytics, Computer Science, Engineering, Economics, or related field. MBA will be a bonus.

• Industry experience in Retail, CPG, BFSI, Healthcare, Travel, or Telecom preferred

• Exposure to MLOps, data engineering, or productionization concepts.

• Experience in business-driven modelling such as:

  • Demand forecasting

  • Churn prediction

  • Customer segmentation

  • Pricing analytics

  • MMM / attribution

  • Risk scoring

  • Fraud detection

Why Join Us? (Unique Value Proposition to Candidate)

• Direct mentorship from a senior analytics leader with deep experience across global CPG and retail analytics, advanced modelling, enterprise AI, and data strategy.

• Opportunity to learn consulting-grade problem formulation, proposal writing, and stakeholder influencing—capabilities rarely offered in technical roles.

• High ownership: architect solutions, shape the roadmap, and build MVPs that reach leadership.

• Be part of a fast-growing team where your work directly impacts business decisions.

• Work on innovative client problems