Job Description

About Digitap.ai:

DIGITAP.AI is an Enterprise SaaS company providing high-tech advanced AI/ML, Alternate Data Solutions to new-age internet-driven businesses for reliable, fast, and 100% compliant Customer Onboarding, Alternate Data Solutions for Automated Risk Management, and other Value-Added Services. Our proprietary Machine Learning Algorithms and Modules provide one of the best success rates in the market. We work with the top digital lenders of India & the team brings together deep and vibrant experience in Fintech Product & Risk Management, Fraud Detection, and Risk Analytics.

Culture and Benefits:

• Innovative Start-up Environment: Enjoy the flexibility to design, implement, and influence the development of cutting-edge solutions.
• Transparency and Meritocracy: We value clear communication, eschew politics, and promote an open culture where contributions are recognized and rewarded.
• Ownership and Impact: We encourage team members to take ownership, think beyond their roles, and contribute to the company's success in meaningful ways.
• Competitive Compensation: We offer a competitive salary and a potential equity package, aligning your success with the company's growth.

As a Data Scientist – Machine Learning, you will design and develop advanced ML models for credit scoring and risk assessment, while also leading research and innovation in large-scale transformer-based systems.

Key Responsibilities:

• Credit & Risk Analytics: Design, develop, and optimize ML models for credit scoring, risk prediction, and scorecard generation.
• Model Deployment & Automation: Implement scalable pipelines for model training, validation, and deployment in production environments.
• Feature Engineering: Identify, extract, and engineer key features from structured and unstructured data to enhance model performance.
• Model Monitoring: Establish continuous monitoring frameworks to track model drift, performance metrics, and data quality.
• Research & Innovation: Explore and apply state-of-the-art ML and transformer architectures to improve predictive accuracy and interpretability.
• Collaboration: Work closely with data engineers, product managers, and domain experts to translate business objectives into robust ML solutions.

Required Skills and Experience:

• Machine Learning: 3+ years of hands-on experience in developing, training, and deploying ML models for structured or tabular data.
• Statistical Modeling: Solid understanding of statistical concepts, feature engineering, and model evaluation techniques.
• ML Frameworks: Experience with scikit-learn, PyTorch, or TensorFlow for building and optimizing predictive models.
• Python Programming: Strong proficiency in Python, with experience using NumPy, Pandas, and Matplotlib for data manipulation and analysis.
• Data Handling: Practical experience with large datasets, data cleaning, preprocessing, and transformation for ML workflows.
• SQL & APIs: Proficiency in writing SQL queries and integrating ML models with APIs or backend systems.
• Version Control & Collaboration: Familiarity with Git and collaborative model development practices.
• Analytical Thinking: Strong problem-solving skills with the ability to translate business problems into data-driven ML solutions.

Preferred Qualifications:

• Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, or a related quantitative field.
• Experience: Min2 years of experience in machine learning, data analytics, or applied statistics roles.
• Cloud Platforms: Exposure to AWS, GCP, or Azure for model deployment or data processing.
• Domain Knowledge: Familiarity with fintech, credit risk, or business analytics domains.
• Automation & MLOps: Basic understanding of model deployment, monitoring, or pipeline automation tools.
• Continuous Learning: Enthusiasm for exploring new ML algorithms, open-source tools, and emerging technologies in data science.