About the job Credit Risk Machine learning Modeling Lead (AVP)
Role : AVP
Location : Norfolk, VA, US (Hybrid ,2-3 days a week work from office)
Role & Responsibilities Overview :
We are looking for analytics professional to lead a modeling team for a client in BFSI domain. You will be responsible for overseeing insights delivery, growth, and expansion within this engagement. You will also collaborate with senior leaders and stakeholders to align & implement roadmap of modeling focused initiatives.
As an engagement lead, you will have the following responsibilities :
- End to end leading a team of data scientists. Provide project, client & team management and monitor progress of deliverables on daily basis and ensures timely resolution of any issues
- Provide technical direction on day to day basis to team of data scientists on data handling, data manipulation, predictive modeling spanning across stages of model development and implementation lifecycle.
- Serve as the functional and domain expert for the modeling team to ensure that they meet client expectations
- Understand the clients business requirements, translate into a business problem and design the methodology to solve the business problem
- Expertise in machine learning model development and sound exposure on ML ops process
- Able to work in dual shore engagement across multiple time zones and must have experience in managing clients directly
- Stay updated on the latest trends and developments in machine learning model development techniques
- Facilitate client working sessions and lead recurring project status meetings
- Capability development identify and productize analytical solutions that can be implemented by different clients
Candidate Profile :
Bachelors degree or higher in statistics, mathematics, computer science, or a related fieldAt least 8-10 years of experience in data analytics and data scienceExperience in credit risk analytics will be preferredExtensive knowledge of building machine learning models from scratch. At least 6+ years experience with special emphasis on the advanced algorithms like bagging, gradient boosting machines, random forests, SVM, K-means, deep learning or reinforcement learningProficiency in manipulating large scale data using Python and SQLKnowledge of MS Azure / AWS services or similar cloud platformsSound understanding of ML ops processProven track record of leading and managing analytics teams and projectsExcellent communication, presentation, and interpersonal skillsAbility to work independently and collaboratively in a fast-paced and dynamic environment