Data Scientist

Credible

5d•$89,000 - $136,000

About The Position

Our Business Intelligence and Analytics team is seeking to grow the company’s Data Science and Machine Learning capabilities across several facets of our business. We are seeking a seasoned Data Scientist to lead this transformative step change function and nurture its growth across several focus areas. You will drive tactical and strategic execution of several data science projects and manage their entire lifecycle. You will: Drive the long-term statistical modeling and machine learning vision of the company. Conducting exploratory data analysis (EDA) prior to model development Performing feasibility assessments (POCs) for proposed ML solutions Monitoring and diagnosing model performance issues, including drift and degradation Researching and evaluating additional use cases for existing models Supporting ad hoc ML-related analytics requests from stakeholders and senior data scientists Conduct analysis to develop and improve our product recommendation and user classification systems. Use this understanding to help optimize our adaptive user experience, cross selling initiatives, and retargeting efforts. Design, prototype, and implement models across several domains. Manage each part of a project’s life cycle, including ad-hoc exploration, preparation of training data, model development, and production deployment. Work with product and marketing managers, analysts, and engineers to turn insights about our users into automated services.

Requirements

BA/BS in Mathematics, Statistics, or Computer Science required. Masters Degree in a quantitative or scientific discipline strongly desired.
3+ years experience in developing, testing, and deploying optimized predictive models (preferably to inform in-product recommendation systems and automated customer re-targeting efforts).
Advanced statistical modeling skillset (Python, R, etc).
Advanced SQL querying, data mining, and data cleansing skillset.
Deep knowledge of supervised and unsupervised machine learning algorithms (neural networks, decision trees, etc.).
Advanced knowledge of experiment design.
Hands-on experience with cloud infrastructure tools (AWS EC2, S3, Redshift, Snowflake, containers).
Advanced visualization/reporting and presentation experience.
Version Control Experience (GitHub).
Demonstrated experience adopting and effectively leveraging AI tools in day-to-day data science workflows.
Familiarity with ML deployment infrastructure (ETL, tools, new products in the space).
Excellent strategic project planning skillset.
Excellent communication skills (written and verbal).
Ability to develop project plans and deliver results against quarterly and annual roadmaps.
Comfortable working with loosely defined requirements where you exercise your creativity and analytical skills to deliver best in class solutions.
Excellent written and verbal communication. Ability to articulate vision, complex thoughts, analytical processes, and results in clear business terms.
Anticipate business needs and think with a business owner mindset – think critically about analyses and solutions and provide improvement recommendations. This role requires an individual who can work autonomously.

Nice To Haves

Experience with Seldon-core or other MLOps tools is preferred.
Experience at an e-commerce or fintech company is a plus.

Responsibilities

Drive the long-term statistical modeling and machine learning vision of the company.
Conducting exploratory data analysis (EDA) prior to model development
Performing feasibility assessments (POCs) for proposed ML solutions
Monitoring and diagnosing model performance issues, including drift and degradation
Researching and evaluating additional use cases for existing models
Supporting ad hoc ML-related analytics requests from stakeholders and senior data scientists
Conduct analysis to develop and improve our product recommendation and user classification systems. Use this understanding to help optimize our adaptive user experience, cross selling initiatives, and retargeting efforts.
Design, prototype, and implement models across several domains. Manage each part of a project’s life cycle, including ad-hoc exploration, preparation of training data, model development, and production deployment.
Work with product and marketing managers, analysts, and engineers to turn insights about our users into automated services.