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<div style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; white-space: normal; text-decoration-style: initial; text-decoration-color: initial;"><p style="box-sizing: border-box; outline: 0px; margin: 0px; color: rgb(0, 0, 0); font-family: -apple-system, BlinkMacSystemFont, ;"><strong><strong>About Digitap.ai:</strong><br></strong></p><p style="box-sizing: border-box; outline: 0px; margin: 0px; color: rgb(0, 0, 0); font-family: -apple-system, BlinkMacSystemFont, ;"><br>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.</p><p style="box-sizing: border-box; outline: 0px; margin: 0px; color: rgb(0, 0, 0); font-family: -apple-system, BlinkMacSystemFont, ;"><br></p><p style="box-sizing: border-box; outline: 0px; margin: 0px; color: rgb(0, 0, 0); font-family: -apple-system, BlinkMacSystemFont, ;"><strong>Culture and Benefits:</strong></p><p style="box-sizing: border-box; outline: 0px; margin: 0px; color: rgb(0, 0, 0); font-family: -apple-system, BlinkMacSystemFont, ;"><br></p><ul><li>Innovative Start-up Environment: Enjoy the flexibility to design, implement, and influence the development of cutting-edge solutions.</li><li>Transparency and Meritocracy: We value clear communication, eschew politics, and promote an open culture where contributions are recognized and rewarded.</li><li>Ownership and Impact: We encourage team members to take ownership, think beyond their roles, and contribute to the company's success in meaningful ways.</li><li>Competitive Compensation: We offer a competitive salary and a potential equity package, aligning your success with the company's growth.</li></ul><div><br></div><div style="box-sizing: border-box; outline: 0px; color: rgb(0, 0, 0); font-family: -apple-system, BlinkMacSystemFont, ;"><h4 style="box-sizing: border-box; outline: 0px; font-family: -apple-system, BlinkMacSystemFont, ;"><strong>Job Description:</strong></h4><p style="box-sizing: border-box; outline: 0px; margin: 0px; color: rgb(0, 0, 0); font-family: -apple-system, BlinkMacSystemFont, ;">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.</p><div><br></div><div style="box-sizing: border-box; outline: 0px; color: rgb(0, 0, 0); font-family: -apple-system, BlinkMacSystemFont, ;"><h4 style="box-sizing: border-box; outline: 0px; font-family: -apple-system, BlinkMacSystemFont, ;"><strong>Key Responsibilities:</strong></h4><ul><li>Credit & Risk Analytics: Design, develop, and optimize ML models for credit scoring, risk prediction, and scorecard generation.</li><li>Model Deployment & Automation: Implement scalable pipelines for model training, validation, and deployment in production environments.</li><li>Feature Engineering: Identify, extract, and engineer key features from structured and unstructured data to enhance model performance.</li><li>Model Monitoring: Establish continuous monitoring frameworks to track model drift, performance metrics, and data quality.</li><li>Research & Innovation: Explore and apply state-of-the-art ML and transformer architectures to improve predictive accuracy and interpretability.</li><li>Collaboration: Work closely with data engineers, product managers, and domain experts to translate business objectives into robust ML solutions.</li></ul><div><br></div><p style="box-sizing: border-box; outline: 0px; margin: 0px; color: rgb(0, 0, 0); font-family: -apple-system, BlinkMacSystemFont, ;"><br></p><h4 style="box-sizing: border-box; outline: 0px; font-family: -apple-system, BlinkMacSystemFont, ;"><strong>Required Skills and Experience:</strong></h4><ul><li>Machine Learning: 3+ years of hands-on experience in developing, training, and deploying ML models for structured or tabular data.</li><li>Statistical Modeling: Solid understanding of statistical concepts, feature engineering, and model evaluation techniques.</li><li>ML Frameworks: Experience with scikit-learn, PyTorch, or TensorFlow for building and optimizing predictive models.</li><li>Python Programming: Strong proficiency in Python, with experience using NumPy, Pandas, and Matplotlib for data manipulation and analysis.</li><li>Data Handling: Practical experience with large datasets, data cleaning, preprocessing, and transformation for ML workflows.</li><li>SQL & APIs: Proficiency in writing SQL queries and integrating ML models with APIs or backend systems.</li><li>Version Control & Collaboration: Familiarity with Git and collaborative model development practices.</li><li>Analytical Thinking: Strong problem-solving skills with the ability to translate business problems into data-driven ML solutions.</li></ul><div><br></div><div style="box-sizing: border-box; outline: 0px; color: rgb(0, 0, 0); font-family: -apple-system, BlinkMacSystemFont, ;"><h4 style="box-sizing: border-box; outline: 0px; font-family: -apple-system, BlinkMacSystemFont, ;"><strong>Preferred Qualifications:</strong></h4><ul><li>Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, or a related quantitative field.</li><li>Experience: Min 3 years of experience in machine learning, data analytics, or applied statistics roles.</li><li>Cloud Platforms: Exposure to AWS, GCP, or Azure for model deployment or data processing.</li><li>Domain Knowledge: Familiarity with fintech, credit risk, or business analytics domains.</li><li>Automation & MLOps: Basic understanding of model deployment, monitoring, or pipeline automation tools.</li><li>Continuous Learning: Enthusiasm for exploring new ML algorithms, open-source tools, and emerging technologies in data science.</li></ul><div><br></div></div></div></div></div>

Data Scientist ML Engineer