Data Scientist GnuGrid CRB Limited

As a Data Scientist, you will play a vital role in the entire model life cycle, engaging stakeholders, building models, and monitoring their performance. Your responsibilities will span various areas, such as customer acquisition, risk management, marketing, and product development, and your impact will extend across different functional teams, including sales, credit, tech, and product.

Key Duties and Responsibilities:
• Design and develop effective credit risk and customer management models for all products and markets.
• Execute and maintain models to optimize profit.
• Provide scorecard monitoring reports to lenders quarterly and as requested.
• Implement customer model decisions to enhance engagement.
• Contribute to product development and GTM strategies, integrating credit quality into product design and market communication.
• Develop benchmarks for financial risk compliance monitoring.
• Oversee resources recruitment, machine learning model build/delivery methodology, governance, and model monitoring and performance framework.
• Coach junior staff on technical solutions and data analysis techniques.
• Champion and implement delivery methods adopted by Gnu grid.
• Collaborate with the technology team to ensure the right infrastructure is in place to support Machine Learning initiatives.

Job Specification:
• Bachelor’s degree or equivalent in Mathematics, Statistics, Computer Science, Physics, Engineering, or related numerate subjects. A Master’s orPh.D. in these fields would be a significant plus.
• 3+ years of professional experience in programming, preferably applied to business problems.
• Good understanding of Credit Bureau Business/banking/financial domain.
• Experience with implementing the full analytics pipeline, including defining business problems, data engineering, feature construction, modeling, and implementation.

Competencies:
Technical Competencies:
• Proven background in computer science, data science, machine learning, artificial intelligence, statistics, or other quantitative and computational sciences.
• Experience in building and delivering data science and statistics-based solutions with measurable results and impact.
• Ability to manage the model-building process, feature incorporation, performance tuning, and deployment under tight time constraints.
• Strong knowledge of machine learning practices, including linear and logistic regression, decision tree classification and regression, neuralnetworks, dimensionality reduction, clustering, bagging, boosting, and other non-parametric methods.
• Expertise in working with and maintaining open-source data analysis
• platforms, such as Scikit-Learn, TensorFlow, R, and other Python data tools, as well as SQL storage tools.

Behavioral Competencies:
• Collaborate effectively in interdisciplinary teams with engineers, designers, business experts, clients, and end-users.
• Proactive self-starter who takes initiative, adapts quickly to new challenges,
• and contributes to problem-solving within a flat team structure.
• Excellent written and verbal communication skills, capable of effectively communicating technical concepts to various audiences with varying levels of technical fluency and seniority.