Data Scientist ML Engineer

About Us: DIGITAP.AI is a cutting-edge provider of AI/ML solutions tailored for the modern, internet-driven business landscape. Our advanced technologies empower businesses with reliable, fast, and fully compliant customer onboarding, automated risk management, and big data-enabled services, including Risk Analytics and Customised Scorecards. Our proprietary machine learning algorithms and modules boast some of the highest success rates in the market. Partnering with the largest digital lenders in India, our team is a vibrant mix of expertise in Fintech Product & Risk Management, Fraud Detection, and Big Data 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. Job Description: 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:  2+ 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.