Job Description

ROLES & RESPONSIBILITIES


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
EXPERIENCE


12-14 Years
SKILLS


Primary Skill: Data Science
Sub Skill(s): Data Science
Additional Skill(s): Python, Power BI, ETL, Data Science, AWS-Apps, Azure-Apps, SQL, Analytics Development
ABOUT THE COMPANY


Infogain is a human-centered digital platform and software engineering company based out of Silicon Valley. We engineer business outcomes for Fortune 500 companies and digital natives in the technology, healthcare, insurance, travel, telecom, and retail & CPG industries using technologies such as cloud, microservices, automation, IoT, and artificial intelligence. We accelerate experience-led transformation in the delivery of digital platforms. Infogain is also a Microsoft (NASDAQ: MSFT) Gold Partner and Azure Expert Managed Services Provider (MSP).



Infogain, an Apax Funds portfolio company, has offices in California, Washington, Texas, the UK, the UAE, and Singapore, with delivery centers in Seattle, Houston, Austin, Krakow, Noida, Gurgaon, Mumbai, Pune, and Bengaluru.