Website FundsIndia

Data Scientist – FundsIndia

Opportunity Overview

Attribute Details
Organisation FundsIndia
Position Title Data Scientist
Reporting Line Directly to the Director, Program Management
Primary Location Bengaluru, Karnataka, India
Experience Level 3 to 4 Years in data science, analytics, or quantitative modelling
Education Target Tier-1 Institutions (IITs, IIMs, ISI, BITS Pilani, or equivalent)
Core Skillset Python, SQL, Applied Statistics, Machine Learning, Portfolio Analytics, A/B Testing

Analytics & Business Impact Lifecycle

The Data Scientist translates complex transactional and portfolio data into operational strategies to optimise investor retention, portfolio alignment, and platform growth:

┌───────────────────────────────────────────┐
│ 1. Data Ingestion & SQL Modeling          │ ➔ Large-scale transactional, SIP, and portfolio dataset extraction
└─────────────────────┬─────────────────────┘
                      ▼
┌───────────────────────────────────────────┐
│ 2. Predictive & ML Model Development     │ ➔ Portfolio drift detection, churn/redemption forecasting, recommendation engines
└─────────────────────┬─────────────────────┘
                      ▼
┌───────────────────────────────────────────┐
│ 3. Hypothesis Testing & Experimentation  │ ➔ A/B testing design, variance tracking, program KPI measurement
└─────────────────────┬─────────────────────┘
                      ▼
┌───────────────────────────────────────────┐
│ 4. Business Storytelling & Strategy       │ ➔ Executive reporting, program management alignment, advisor toolings
└───────────────────────────────────────────┘

Key Responsibilities

  • Investment & Portfolio Analytics: Construct statistical models to detect portfolio drift, track risk-return metrics, and generate fund-level insights for advisors and retail investors.

  • Predictive & Growth Forecasting: Develop predictive machine learning models to forecast platform revenue, customer churn, redemption risks, and Systematic Investment Plan (SIP) continuity.

  • Personalization & Recommendation: Build customer segmentation and recommendation engines to personalize user engagement and increase conversion and lifetime value.

  • Program Management Partnership: Collaborate directly with Program Management to establish measurement frameworks, track program KPIs, and quantify the ROI of new platform initiatives.

  • Experimentation & Storytelling: Design and evaluate A/B tests to validate product hypotheses; present technical outcomes as clear strategic recommendations to executive leadership.

Candidate Qualifications & Skill Matrix

Baseline Qualifications

  • Educational Background: Bachelor’s or Master’s degree from a Tier-1 institution (IITs, IIMs, ISI, BITS Pilani, or top-tier equivalent) in Computer Science, Statistics, Mathematics, Engineering, or Economics.

  • Experience Range: 3 to 4 years of hands-on data science or quantitative analytics experience working with large-scale datasets.

Technical & Functional Skill Matrix

Competency Area Primary Capabilities & Tools
Programming & Data Processing Advanced Python (Pandas, NumPy, Scikit-Learn) and complex SQL query design.
Statistical & ML Modeling Time-series forecasting, churn prediction, classification, regression, and clustering algorithms.
Experimentation Frameworks Hypothesis testing, sample size calculation, A/B test design, and statistical variance analysis.
Business Intelligence & Reporting Building production dashboards, KPI measurement frameworks, and executive reports.
Domain Exposure Wealth management, mutual funds, fintech, portfolio risk metrics, or consumer finance analytics.

Application & Assessment Preparation

  1. Portfolio & FinTech Metrics: Review quantitative financial metrics such as portfolio drift, Asset Under Management (AUM) growth, churn rates, and SIP retention loops.

  2. Experimentation & A/B Testing: Be prepared to answer questions on experimental design, statistical significance, sample size estimation, and handling novelty bias in product rollouts.

  3. Business Communication Case Studies: Practice presenting a complex data modeling project, detailing how your technical findings directly drove business decisions and revenue growth.

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