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Lead - Investment Analytics

Aditya Birla Capital

Maharashtra, India ・ フルタイム

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経験
8–12 yrs
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11時間前

Where you'll work

仕事内容

Role overviewAditya Birla Capital Digital is seeking a senior analytics leader to own the investment analytics agenda end to end across mutual funds, wealth, and other investment offerings. The mandate is to use data and analytics to improve acquisition, portfolio expansion, customer engagement, and advisory effectiveness, with a strong emphasis on personalization and customer lifetime value.This is a hands-on leadership position. It calls for deep analytics and data science capability, solid knowledge of investment products, and the confidence to influence senior stakeholders while developing a high-performing team.Location: Thane / Mumbai, Maharashtra, India.About Aditya Birla CapitalAditya Birla Capital Limited is the holding company for the financial services businesses of the Aditya Birla Group. Through its subsidiaries and joint ventures across protection, investment, and financing solutions, the organization supports customers through different stages of their financial journey.The company operates at large scale with support from more than 34,000 employees, over 1,200 branches, and around 2,00,000 partners. It is part of the wider Aditya Birla Group, which spans 36 countries and employs over 140,000 people.About Aditya Birla Capital DigitalAditya Birla Capital Digital, established in 2023, is a wholly owned subsidiary focused on digital distribution and customer engagement in financial services.It is building a direct-to-customer omni-channel experience through app and web journeys.The platform is designed to support investing, lending, insurance, and payments in one connected experience.It enables cross-sell, digital servicing, and personalized engagement across app, web, calling, and branch channels.The team relies on data, technology, and analytics to create a scalable digital financial services platform.Role focusThe position is centered on investment analytics and data science for businesses spanning mutual funds, wealth, broking, PMS, and AIF. The goal is to unlock growth and engagement through actionable insights and smarter customer journeys.You will contribute to growth in the investments business by improving acquisition and activation, increasing assets under management, strengthening customer stickiness, deepening portfolios, and enabling personalized advisory experiences.Key responsibilitiesSet the analytics direction for investments across mutual funds, wealth, and advisory, ensuring initiatives support AUM growth, engagement, and revenue goals.Lead acquisition and activation programs such as investor onboarding, SIP conversion, first-time investment journeys, and re-engagement of inactive users.Create portfolio and lifecycle analytics structures covering segmentation, investor behavior, retention, churn, and lifetime value.Power personalized investment journeys using next-best-action and next-best-offer logic, goal-based suggestions, and recommendations aligned to risk profiles.Build and operationalize machine learning models including propensity-to-invest, SIP continuation probability, churn forecasting, portfolio optimization signals, and product affinity.Work with product and advisory teams to embed analytics into customer journeys such as portfolio insights, rebalancing prompts, and wealth recommendations.Apply an AI-led approach using GenAI-supported insights, smart dashboards, and conversational analytics for business users, with strong attention to governance and compliance.Improve AUM growth and engagement through targeted campaigns, cohort-based strategies, and omni-channel activation across app, web, relationship managers, tele-calling, and partners.Design experimentation approaches such as A/B tests and cohort analysis to measure and optimize campaigns, nudges, and advisory actions.Partner with data engineering teams to develop investment-specific datasets, a customer 360 view, and scalable data models.Define KPI and reporting structures covering funnel metrics, SIP starts, repeat investments, AUM growth, and campaign return on investment.Maintain adherence to regulatory and compliance requirements, including SEBI guidelines, suitability standards, and data privacy expectations.Recruit, guide, and lead an analytics team, building capability in investment analytics and maintaining high delivery standards.What you bringA bachelor’s or master’s degree in Mathematics, Statistics, Engineering, Finance, or another quantitative discipline.8 to 12 years of experience in analytics or data science, with strong exposure to investment, wealth, or mutual fund analytics.Clear understanding of investment products, investor behavior, and wealth management journeys.Practical experience with Python, SQL, and PySpark, along with modern data platforms and cloud-based environments.Background in customer segmentation, lifecycle analytics, campaign analytics, and personalization frameworks.Exposure to portfolio analytics, risk profiling, and investment advisory models is preferred.An AI-oriented mindset, including experience with GenAI or advanced analytics tools for business decision-making.Ability to turn business challenges into analytical solutions and execute initiatives from concept to delivery.Strong stakeholder management skills and a track record of influencing senior leaders.Experience in building, coaching, and managing analytics teams.Why this role stands outJoin a fast-growing digital financial services business backed by a strong enterprise ecosystem.Own a high-impact mandate in investments with direct influence on AUM growth and customer engagement.Work across multiple business lines using rich datasets and omni-channel customer journeys.Help shape investment analytics, advisory, and personalization capabilities from an early stage.Lead and scale a strong analytics team while driving innovation in AI-led financial services.Additional informationThis is a full-time, onsite leadership role in Maharashtra, India.No stipend or salary details were provided in the source.

スキル

SQL Python 機械学習 データサイエンス ステークホルダーマネジメント PySpark Customer Segmentation A/B Testing Investment analytics Lifecycle analytics Portfolio analytics Risk profiling

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