Principal Data Scientist

We are looking for a hands-on Principal Data Scientist to join our Analytics & Data Science team. Your mission is to drive significant business value and innovation by spearheading our most critical machine learning initiatives and establishing a center of excellence for the data science practice.

This is more than a modeling role; you will be a thought leader and a key driver in our journey to operationalize advanced analytics. Your work will directly influence customer growth, product strategy, and marketing effectiveness, tangibly impacting our revenue and our customers' financial lives.

As a hands-on leader, you will own high-impact initiatives from concept to production, setting the standard for how data science creates value at Self. Examples include:

• Revolutionize Marketing & Partnership Strategy: Spearhead the development of predictive LTV and customer quality models for our core products. Your work will enable robust, timely budgeting and optimization decisions, directly improving Marketing ROAS.
• Engineer the Next Best Action & Communication: Partner with Lifecycle Marketing and Product to architect our next-generation recommendation engine. You will develop models that determine the optimal action, message, channel, and frequency to improve customer activation and success, serving as the core of our lifecycle channel optimization.
• Predict and Prevent Customer Churn: Design and deploy models that predict which new members are at risk of becoming inactive and build comprehensive models to predict churn across the entire customer lifecycle, enabling proactive retention strategies.
• Lead with an AI-First Mindset: Champion an AI-first approach to advance our analytical capabilities and business productivity. You will be responsible for identifying and spearheading the development of high-impact AI solutions, from creating innovative, customer-facing features to building internal tools that accelerate company-wide decision-making.
• Maturing A Center of Excellence: Be the catalyst for maturing our data science capabilities. You will partner closely with leaders in Credit Risk, MLOps, and Architecture to define and implement company-wide standards, reusable patterns, and best practices for the entire ML lifecycle.

• Are a Builder and a Strategist: You are equally comfortable architecting a multi-year technical vision and getting your hands dirty coding the solution. You embrace ownership and drive initiatives to completion.
• Connect Your Work to Business Impact: You have a proven track record of developing ML models that deliver measurable revenue growth and customer value. You care deeply that your work creates meaningful change.
• Thrive in Ambiguity: You are an expert at navigating complex, open-ended problems and carving a clear path to a solution. You are comfortable in a fast-paced environment where you have the autonomy to make key decisions.
• Are a Force Multiplier: You are an excellent communicator who can articulate complex concepts to technical and non-technical audiences alike, influencing stakeholders and mentoring fellow team members.

Core Qualifications:

• 7+ years of hands-on experience in data science and machine learning, with a portfolio of models deployed in production that have driven significant business outcomes.
• Expert-level proficiency in Python and data manipulation libraries (Pandas, NumPy), as well as SQL for complex data extraction and analysis.
• Deep, practical experience across a range of ML techniques, such as propensity modeling, lead scoring, clustering, recommendation systems, and causal inference.
• Proven experience owning the end-to-end ML lifecycle, from feature engineering on large, complex datasets to partnering with MLOps/Engineering for deployment and monitoring.
• Experience with version control systems (Git).

Preferred Qualifications:

• Advanced degree (Masters or PhD) in a quantitative field like Computer Science, Statistics, Physics, or Mathematics.
• Experience in the fintech or financial services industry.
• Familiarity with cloud-based ML ecosystems, particularly AWS (S3, Glue, Redshift, SageMaker).
• Experience developing or working with nascent AI capabilities (e.g., leveraging LLMs for internal tools, GenAI).