Sr. Analyst, Data Science

LPL Financial•Fort Mill, SC

About The Position

We are seeking a curious and analytically rigorous Senior Analyst, Data Science to uncover key insights that drive strategic decisions and product development for Growth Strategy & Enablement (GS&E). This role is ideal for a data scientist who is equally comfortable writing code, building models, and communicating findings to non-technical stakeholders. You’ll be part of the growing GS&E Data Science team. You’ll closely collaborate with the team and other key business partners to frame analytical problems, design and execute analyses, and translate results into actionable recommendations. This is a high-impact, hands-on role for someone who wants to apply classical data science methods—machine learning, statistics, and causal inference—in a fast-moving, mission-driven environment.

Requirements

2–4 years of experience in a data science, quantitative analysis, or applied research role in a business setting.
Proficiency in Python for data manipulation, statistical analysis, and machine learning, that goes beyond Jupyter notebooks; strives for clean, Git version-controlled code.
Solid grounding in statistics, probability, and machine learning fundamentals.
Hands-on experience with causal inference methods and experimental design.
Experience working with large-scale data in SQL & Snowflake; comfortable building and maintaining clean, reproducible data pipelines as needed to support modeling and analysis work.
Data visualization skills and ability to communicate findings clearly to non-technical stakeholders; note this role will not be focused on developing dashboards.
Bachelor’s degree in Statistics, Mathematics, Computer Science, Economics, or a related quantitative field required.

Nice To Haves

Financial services experience is a plus but not required.
Master’s degree preferred.

Responsibilities

Design and execute end-to-end analyses that surface meaningful business insights, from data extraction and cleaning through modeling and interpretation.
Apply statistical methods—including hypothesis testing, regression, and causal inference—to answer business questions with rigor and clarity.
Translate complex analytical outputs into clear narratives and visualizations for business stakeholders and senior leadership.
Build, validate, and deploy supervised and unsupervised machine learning models to support segmentation, prediction, and optimization use cases.
Evaluate model performance using appropriate metrics and communicate trade-offs and assumptions to both technical and non-technical audiences.
Stay current on advances in applied ML and bring emerging methods to bear on relevant business problems.
Design and analyze A/B tests and observational studies to identify causal relationships and measure the impact of business initiatives.
Apply quasi-experimental methods when randomized experiments are not feasible.
Partner with business teams to build a culture of evidence-based decision-making.
Work closely with data engineers, product managers, and business stakeholders to access, understand, and leverage data assets across the enterprise.
Document analytical workflows, assumptions, code and findings to ensure reproducibility and knowledge sharing across the team.
Contribute to building a scalable data science practice by identifying opportunities to improve tools, processes, and methodologies.