Solytics Partners - Data Scientist - Banking Domain (6-12 yrs)

Job Overview:

We are seeking a talented and driven Machine Learning Engineer to design and optimize machine learning models that will enhance a variety of credit and operational processes. This role will involve building cutting-edge solutions in areas like underwriting, loan processing, fraud detection, and risk mitigation, while continuously driving improvements through innovation and advanced techniques.

Key Responsibilities:

- Model Development & Optimization: Design, deploy, and refine machine learning models to support key functions such as underwriting, account management, fraud detection, and loss prevention.

- Advanced Techniques Implementation: Identify opportunities to implement advanced machine learning approaches-such as Natural Language Processing (NLP), Image Recognition, and Graph Mining-to address complex business challenges.

- Collaboration & Knowledge Sharing: Work with internal and external resources, as well as open-source algorithms, to find the best solutions and improve model performance.

- Risk Management Compliance: Partner with the Model Risk Management team to ensure models are developed with appropriate rigor and align with risk management and governance standards.

- Cross-Functional Collaboration: Collaborate closely with Product and Engineering teams to ensure seamless model deployment and integration.

- Performance Monitoring: Track the effectiveness of models through dashboards and key performance indicators (KPIs), ensuring continuous improvement.

- Stakeholder Communication: Present model insights and performance reports to key stakeholders in Credit, Risk, and Business Units, making complex findings accessible and actionable.

Qualifications:

- Educational Background: Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field. A Master's degree is highly preferred.

- Experience: 6-12 years of hands-on experience developing and implementing machine learning models in a professional environment.

- Technical Proficiency: Strong expertise in SQL and Python, with the ability to write efficient code for working with large datasets and creating advanced features.

- Analytical Thinking: Exceptional attention to detail and an ability to uncover hidden data patterns through thorough investigation.

- Machine Learning Expertise: Solid foundation in machine learning and statistical modeling, including supervised and unsupervised learning techniques like regression, decision trees, support vector machines, boosting, deep learning, and more. Must stay current with industry advancements to apply the best techniques.

- Database Knowledge: Proficient in working with SQL, NoSQL, Hive, and other relevant database technologies.

- Effective Communication: Strong ability to translate complex technical concepts into clear, understandable terms for non-technical stakeholders.