Job Description
Data Science Manager
Location: Addison, TX
Salary: 170,000 - 190,000 annual, plus bonus
Schedule: Hybrid, Monday-Friday, 2-3 days/week onsite, remainder remote
Employment Status: Must be able to work direct on W2 basis, no Visa transfers
Innovative company specializing in advanced financial solutions aimed at optimizing day-to-day financial management is hiring a Data Science Manager for a permanent role. The Data Science team is responsible for creating, implementing, and managing predictive models leveraging sophisticated statistical methods and machine learning algorithms. These models serve key business areas, including Underwriting, Account Management, and Operations, helping to drive growth, manage risk, and enhance operational efficiency.
JOB DESCRIPTION
Data Science Manager, you will lead a team of skilled data scientists, utilizing state-of-the-art modeling techniques to promote business growth, optimize risk management, and ensure seamless operations. This position requires a deep understanding of predictive analytics and machine learning, along with the ability to communicate technical insights effectively to business partners and stakeholders.
PRIMARY RESPONSIBILITIES
- Lead a team focused on developing and implementing advanced predictive models and analytical solutions, ensuring high-quality execution and delivery of innovative approaches.
- Design, build, and deploy machine learning and AI-driven algorithms for applications in areas such as Underwriting, Customer Management, Marketing, and Operational Efficiency.
- Act as the primary liaison with internal business units to align data science strategies with the overarching goals and needs of various departments and teams.
- Manage data preparation tasks, including cleaning, merging, and analyzing large data sets using tools like Python, Spark, and Snowflakes to adhere to established data manipulation standards.
- Develop and implement a variety of machine learning algorithms (linear, nonlinear, etc.) for testing and deployment in the underwriting engine to improve risk assessment and management across multiple acquisition channels.
- Apply data mining techniques to minimize risks, such as credit and fraud losses, and enhance response and approval rates, ultimately driving profitability for financial products.
- Lead the implementation of scoring models across multiple platforms, including cloud-based systems.
- Provide expert guidance on third-party data sources (e.g., TransUnion, Experian, Equifax), including product selection, cost-benefit analysis, variable usage, and data quality assessment.
- Maintain thorough and accessible model documentation using tools like Jupyter Notebook and Rmarkdown to ensure reproducibility and clear communication within the team.
Required Experience and Qualifications:
- Master's degree in a quantitative field such as Statistics, Mathematics, Economics, or Engineering. A Ph.D. is preferred.
- A minimum of six (6) years' experience in data science or modeling, with at least two years of experience leading / managing high-performing, quantitative teams.
- Strong communication skills, with the ability to collaborate effectively with cross-functional teams, including senior management and risk professionals.
- Proven track record in a fast-paced environment, managing evolving demands.
- Expertise in advanced statistical modeling, machine learning techniques (e.g., Random Forest, Gradient Boosting, LASSO, Elastic Net), and familiarity with penalized regression and classification models.
- Advanced data manipulation skills, with proficiency in cleaning, transforming, and engineering data to drive meaningful insights.
- Technical proficiency in programming languages such as Python, R, or Java; experience with version control systems like Git, and familiarity with big data technologies and frameworks such as Spark, Hadoop, and NoSQL.
- Experience with data storage and processing technologies like Snowflakes, Apache Hive/Impala, and JSON/XML parsing.
Job Tags
Permanent employment, Remote job, 2 days per week, 3 days per week, Monday to Friday,