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 field
• At least 8-10 years of experience in data analytics and data science
• Experience in credit risk analytics will be preferred
• Extensive 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 learning
• Proficiency in manipulating large scale data using Python and SQL
• Knowledge of MS Azure/AWS services or similar cloud platforms
• Sound understanding of ML ops process
• Proven track record of leading and managing analytics teams and projects
• Excellent communication, presentation, and interpersonal skills
• Ability to work independently and collaboratively in a fast-paced and dynamic environment