[Hiring] Data Scientist @AccelOne

Role Description

As a Data Scientist within the AI & Data Center of Excellence, you will design and deliver advanced analytical and machine learning solutions that directly influence core financial decision-making across lending, risk, collections, and customer engagement. This role requires a strong blend of statistical rigor, business acumen, and production-oriented thinking, with a clear focus on financial services use cases. You will work closely with cross-functional teams to build scalable models that generate measurable business impact in highly regulated financial environments.

Experience Bands

• Senior Data Scientist: 8–10 years of experience
• Mid-Level Data Scientist: 5–7 years of experience

Key Responsibilities

• Develop and deploy machine learning models across critical financial use cases, including:
  • Credit risk scoring
  • Fraud detection
  • Customer segmentation and Customer Lifetime Value (CLV)
  • Collections optimization
• Translate complex business problems into analytical frameworks and measurable outcomes
• Perform exploratory data analysis on structured and unstructured datasets (e.g., transactions, call logs, financial records, documents)
• Design scalable machine learning pipelines in collaboration with Data and AI Engineering teams
• Lead model validation, explainability, and regulatory compliance processes (e.g., IFRS9, Basel guidelines)
• Build reusable data science components, models, and accelerators
• Present insights, recommendations, and model performance results to senior stakeholders

Financial Services Use Cases (Mandatory Exposure)

• Credit underwriting models (Retail, MSME, or Microfinance)
• Fraud detection and Anti-Money Laundering (AML) analytics
• Early Warning Systems (EWS) for credit risk monitoring
• Collections prioritization and recovery optimization models
• Customer 360 analytics and personalization strategies

Technical Skills

• Programming Languages:
  • Python (mandatory)
  • R or Scala (optional)
• Machine Learning Frameworks:
  • Scikit-learn
  • TensorFlow
  • PyTorch
  • XGBoost
• Advanced Techniques:
  • Deep Learning
  • Natural Language Processing (NLP)
  • Time Series modeling
  • Graph Analytics
• Data Platforms:
  • SQL
  • Spark
  • Hive
  • Big Data ecosystems
• Cloud Platforms:
  • AWS
  • Azure
  • Google Cloud Platform (GCP)

Preferred

• Exposure to Large Language Models (LLMs) and applied AI solutions

Evaluation Criteria

• Depth of real-world deployed use cases (beyond experimentation or academic projects)
• Demonstrated business impact (e.g., revenue improvement, risk reduction, operational efficiency)
• Experience managing the full model lifecycle (development → deployment → monitoring)
• Understanding of financial services and risk-based decision-making environments

Key Performance Indicators (KPIs)

• Model accuracy, stability, and explainability
• Measurable business impact (e.g., NPL reduction, fraud detection improvement)
• Speed and efficiency in delivering production-ready machine learning solutions
• Reusability and scalability of developed analytical assets

Preferred Profile

• Previous experience working in financial institutions such as Banks, NBFCs, or Microfinance organizations
• Strong communication skills with the ability to explain complex technical concepts to business stakeholders
• Ability to operate effectively in cross-country or distributed team environments
• Strong ownership mindset and results-oriented approach