Location: Norfolk, VA, US (Hybrid ,2-3 days a week work from office)
We are seeking an experienced Analytics Professional to lead a modeling team for one of our Fortune 500 client in the Banking, Financial Services, and Insurance (BFSI) domain. This role involves overseeing insights delivery, driving growth, and ensuring the continued expansion. The successful candidate will collaborate with senior leaders and stakeholders to align and implement a roadmap for modeling-focused initiatives.
Key Responsibilities
Lead a team of data scientists: Manage the end-to-end delivery of projects, including team and client management. Oversee daily progress of deliverables, resolving issues efficiently to ensure timely execution.
Provide technical guidance to data scientists on data handling, manipulation, and predictive modeling across all stages of model development and implementation.
Serve as the functional and domain expert for the modeling team, ensuring alignment with client expectations and delivering high-quality outcomes.
Translate client business requirements into solvable business problems, and design tailored methodologies to address these challenges.
Bring expertise in machine learning model development and demonstrate a strong understanding of the ML Ops process.
Manage engagements across multiple time zones, working closely with clients in a dual-shore setup.
Stay updated on the latest machine learning trends and advancements in model development techniques.
Lead client-facing sessions, including working sessions and recurring project status meetings.
Drive capability development, identifying and developing scalable analytical solutions that can be applied across various clients.
Key Qualifications
Bachelor's degree or higher in statistics, mathematics, computer science, or a related field.
8-10 years of experience in data analytics and data science.
Experience in credit risk analytics (preferred).
Extensive knowledge of building machine learning models from the ground up, with at least 6+ years of experience in advanced algorithms, such as:
Bagging
Gradient Boosting Machines
Random Forests
SVM
K-Means
Deep Learning
Reinforcement Learning
Proficiency in working with large-scale data using Python and SQL.
Familiarity with MS Azure, AWS, or other cloud platforms.
In-depth knowledge of the ML Ops process and its application.
Proven track record in leading and managing analytics teams and large-scale projects.
Excellent communication and interpersonal skills, with the ability to present complex insights clearly.
Capable of working both independently and collaboratively in a fast-paced, dynamic environment.