Head of Analytics at Credit Card processing solutions for banks & NBFCs · Bengaluru (Bangalore) · 7 - 15 years · Posted 10 Jul 2023
Data Analytics
Machine Learning (ML)Role: Head of Analytics
Location: Bangalore (Full time)
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ABOUT THE COMPANY WE ARE HIRING FOR:
Our client is offering credit card solutions for banks and financial institutions. It provides services like credit card design and onboarding, credit card authorization, payment processing, collections and dispute resolutions, credit card fraud detection, and more. They serve in the B2B space in the FinTech market segments.
POSITION OVERVIEW
We are seeking an experienced individual for the role of Head of Analytics. As the Head of Analytics, you will be responsible for driving data-driven decision-making, implementing advanced analytics strategies, and providing valuable insights to optimize our credit card business operations, sales and marketing, risk management & customer experience. Your expertise in statistical analysis, predictive modeling, and data visualization will be instrumental in driving growth and enhancing the overall performance of our credit card business.
Responsibilities:
1. Develop and implement Analytics Strategy:
o Define the analytics roadmap for the credit card business, aligning it with overall
business objectives.
o Identify key performance indicators (KPIs) and metrics to track the performance
of the credit card business.
o Collaborate with senior management and cross-functional teams to prioritize and
execute analytics initiatives. 2. Lead Data Analysis and Insights:
o Conduct in-depth analysis of credit card data, customer behavior, and market trends to identify opportunities for business growth and risk mitigation.
o Develop predictive models and algorithms to assess credit risk, customer segmentation, acquisition, retention, and upsell opportunities.
o Generate actionable insights and recommendations based on data analysis to optimize credit card product offerings, pricing, and marketing strategies.
o Regularly present findings and recommendations to senior leadership, using data visualization techniques to effectively communicate complex information.
3. Drive Data Governance and Quality:
o Oversee data governance initiatives, ensuring data accuracy, consistency, and
integrity across relevant systems and platforms.
o Collaborate with IT teams to optimize data collection, integration, and storage
processes to support advanced analytics capabilities.
o Establish and enforce data privacy and security protocols to comply with
regulatory requirements.
4. Team Leadership and Collaboration:
o Build and manage a high-performing analytics team, fostering a culture of innovation, collaboration, and continuous learning.
o Provide guidance and mentorship to the team, promoting professional growth and development.
o Collaborate with stakeholders across departments, including Marketing, Risk Management, and Finance, to align analytics initiatives with business objectives.
5. Stay Updated on Industry Trends:
o Keep abreast of emerging trends, techniques, and technologies in analytics, credit
card business, and the financial industry.
o Leverage industry best practices to drive innovation and continuous improvement
in analytics methodologies and tools.
Qualifications:
Bachelor's or master’s degree in Technology, Mathematics, Statistics, Economics, Computer Science, or a related field.
Proven experience (7+ years) in leading analytics teams in the credit card industry.
Strong expertise in statistical analysis, predictive modelling, data mining, and segmentation techniques.
Proficiency in data manipulation and analysis using programming languages such as Python, R, or SQL.
Experience with analytics tools such as SAS, SPSS, or Tableau.
Excellent leadership and team management skills, with a track record of building and developing high-performing teams.
Strong knowledge of credit card business and understanding of credit card industry dynamics, including risk management, marketing, and customer lifecycle.
Exceptional communication and presentation skills, with the ability to effectively communicate complex information to a varied audience.
Shubham Vishwakarma
Full Stack Developer - Averlon
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Data ScienceThe Role
Own end-to-end credit & fraud data science: feature engineering from raw bureau JSON ,SMS,DEVICE, scorecard / model development, Business Rule Engine (BRE) design, monitoring, and partnering with product/engineering to put rules live. You will work directly with the existing DS team,Tech,product and founders — decisions are data-backed and debated.
What you will own
- Build and maintain credit scorecards and models for FTB and Repeat Borrowers (Xgboost, Random forest, Support Vector Machine Models, ensemble models, challenger models).
- Engineer features from raw CRIF (or equivalent) bureau JSON — tradelines, enquiries, DPD histories, identity matches — and from raw SMS / FinBox alt-data (collections, rejections, salary, app footprint).
- Design, validate, and ship Models: hard rejects, soft flags, amount caps — with clear lift/capture
/ approval trade-offs.
- Own portfolio risk analytics: vintage / DPD / non-starter / POS bad-rate monitoring; propose tier pauses, cool-offs, and ladder-up changes.
- Build fraud signals (device, SIM/OTP, mule, ring, post-disbursal disappearance) and help prioritise the fraud PRD backlog into production.
- Partner with engineering to productionise features, rules, and models (Watchtower-style shadow underwriting, policy index, monitoring dashboards).
- Challenge and refine existing tier/ladder policy with evidence; communicate clearly to founders and business.
Required experience
- Tenure: 5+ years overall experience in data science/analytics.
- Digital lending: Minimum 3 years hands-on in digital lending/consumer credit (NBFC, fintech lender, digital/STPL/) who has built models themselves.
- Scorecards/models: Built and deployed at least one credit scorecard (first-time borrower or repeat borrower, or combined model) into a live BRE / LOS. Should improve approval–bad-rate trade-offs from production experience.
- Bureau: Parsed and engineered features from raw bureau files (CRIF / CIBIL / Experian JSON or XML) — not only vendor-precomputed attributes.
- Non-starter models: Fraud/non-starter / First Payment default modelling experience in short-tenure lending.
- Limit Assignment: Experience with repeat-borrower ladder / limit-management policies.
- Monitoring and QC: Shadow underwriting/champion–challenger frameworks.
- Alt-data: Worked with SMS / alt-data / device / AA signals for underwriting or fraud (FinBox, similar vendors, or in-house SMS parsing).
- Stack: Strong SQL + Python (pandas, sklearn/Logistic / lightgbm/Xgboost/randomforest, statsmodels). Able to write production-quality notebooks and scripts, not just slide decks.
- Communication: Comfortable debating policy with founders/credit heads using data; owns the "show me the evidence" conversation.
Nice to have:
- Feature stores, Airflow/cron pipelines, S3 + Postgres + DynamoDB.
- Prior Experience: Prior work at a zero-to-one digital lender or STPL product.
What success looks like in 6 months
- A documented feature dictionary from raw bureau + SMS with IV/KS ranking.
- At least one new scorecard/model live with clear expected vs observed bad-rate impact.
- Non-starter / First Payment Defaults monitoring with actionable rule recommendations and clear demonstrated improvements in defaults
- Credible pushback on weak policy ideas — backed by analysis, not opinion.
Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon