About the Company: The Financial Broking Pioneer

SBICAP Securities Limited (SSL) is a wholly-owned subsidiary of SBI Capital Markets Limited and a proud member of the State Bank of India (SBI) group—the nation’s largest and most trusted financial banking conglomerate. Operating as a premier financial services company in India’s competitive broking landscape, SBICAP Securities serves a massive, diverse base of retail, institutional, corporate, and NRI clients.

The firm delivers a comprehensive, state-of-the-art investment suite spanning equities, derivatives, mutual funds, depository services, and primary market offerings. Backed by a rigorous corporate heritage and fueled by rapid fintech modernisation, the organisation leverages advanced data science and cloud-ready analytical systems to drive transactional efficiency, enhance market compliance, protect investor trust, and create stable financial solutions for millions of investors nationwide.

About the Role: Financial Data Analyst (Data Science & Analytics)

Are you an analytically minded professional looking to deploy machine learning algorithms and statistical models at the absolute center of India’s capital markets? SBICAP Securities is seeking an experienced, numbers-driven Data Analyst to join our high-impact Data Science & Analytics division at our corporate headquarters in Mumbai (Marathon Futurex, Lower Parel).

This role requires a quantitative practitioner with a solid 2–5 year track record of managing multi-structured financial, client, and transactional data lakes. Moving far past surface-level spreadsheet maintenance, you will be responsible for translating complex market and trading behaviours into actionable, empirical business logic.

Your day-to-day operations will involve building predictive models to forecast client churn and trading volumes, executing rigorous time-series analyses on volatile market price movements, and engineering fraud and anomaly detection models to safeguard enterprise regulatory compliance.

Key Responsibilities & Quantitative Workflows

• Predictive Financial Modelling: Architect, evaluate, and scale predictive statistical models to calculate client churn probability, forecast brokerage revenue, and optimise cross-sell pipelines.

• Surveillance & Risk Engineering: Design and implement automated machine learning workflows for fraud prevention, anomaly detection, and real-time transaction monitoring to satisfy strict regulatory norms.

• Market & Time-Series Analysis: Execute highly optimised time-series calculations on complex market volumes, pricing movements, and structural margin configurations.

• Client Segmentation Matrices: Construct algorithmic data frameworks to isolate macro client cohorts, enabling personalised trading interfaces and optimised pricing strategies.

• BI Dashboard Engineering: Build, manage, and distribute interactive data visualisation pipelines and executive reports using Power BI, Tableau, or Seaborn to steer leadership decisions.

• Data Governance & Quality Control: Perform exhaustive exploratory data analysis (EDA), database cleansing, and structural querying to preserve absolute data integrity across relational databases.

  Candidate Prerequisites & Technical Matrix

  We are looking for an independent, fast-learning analytics professional who can seamlessly translate multi-tiered trading anomalies into stable, production-grade business insights.

  Minimum Required Qualifications:

  • Educational Track: Bachelor’s degree (Any Graduate) from an accredited university with a foundational focus on computer science, economics, finance, statistics, mathematics, or an equivalent quantitative track.

  • Experience Horizon: 2 to 5 years of verified professional experience working directly within a core data analyst, machine learning engineer, or financial business intelligence role.

  • Advanced Programming: Strong proficiency in writing clean data modelling scripts inside Python or R.

  • Data Architecture Core: Exceptional capabilities in writing structured queries (SQL) to manipulate and extract insights from relational database management systems.

  • Mathematical Foundations: Solid operational grasp of mathematical statistics, linear algebra, and probability distributions.

  • Libraries & Frameworks: Practical hands-on experience utilising core computational libraries including Pandas, NumPy, Scikit-learn, and foundational deep learning frameworks like TensorFlow or PyTorch.

  • Visualisation Stack: Direct experience deploying interactive metric dashboards using Tableau, Power BI, Matplotlib, or Seaborn.

  Key Behavioural Competencies

  • Business Translation: The innate ability to deconstruct open-ended commercial problems and translate them into mathematically sound analytical solutions.

  • Stakeholder Management: Exceptional communication and storytelling skills to present high-level risk and revenue metrics clearly to cross-functional technology, compliance, and corporate teams.

  • Regulatory Discipline: A high degree of personal accountability toward maintaining strict data quality control, data privacy standards, and financial compliance parameters.