Job Description:
As a Data Scientist, you will lead the exploration, modeling, and analysis of large, complex datasets to derive actionable insights and power decision-making at scale. Your work will span exploratory data analysis, experimentation, predictive modeling, and advanced statistical techniques, using data to uncover opportunities, mitigate risks, and improve customer and operational outcomes.
You will play a critical role in building scalable data pipelines, designing and validating models, and partnering with product managers, engineers, and executives to embed analytics into strategic initiatives. You’ll not only solve for what happened and why—but also for what’s likely to happen and how we should respond.
This role requires strong business acumen, fluency in ML and statistical techniques, and the ability to operate autonomously in a fast-paced, experimentation-driven environment. Success in this role is measured not only by technical output but by your ability to turn models and analysis into tangible, business-aligned value.
Responsibilities:
Exploratory Data Analysis and Statistical Insight
-
Perform deep data profiling and statistical analysis to extract trends, patterns, and anomalies.
-
Use hypothesis testing and inferential statistics to support business experimentation and performance evaluation.
Predictive and Prescriptive Modeling
-
Design and train machine learning models for classification, regression, recommendation, segmentation, and optimization.
-
Select appropriate modeling techniques (e.g., decision trees, ensembles, neural networks, clustering) based on problem domain and data quality.
-
Ensure model interpretability, stability, and compliance with relevant policies and regulations.
Experimentation and Causal Inference
-
Design and analyze controlled experiments (A/B, multivariate tests) to evaluate the impact of product and policy changes.
-
Apply advanced techniques for causal inference when experiments are impractical (e.g., synthetic controls, difference-in-differences).
Data Engineering and Pipeline Support
-
Collaborate with data engineers to build scalable data pipelines and feature stores for analytics and modeling.
-
Write clean, maintainable, and efficient code for data wrangling, transformation, and automation.
Insight Communication and Decision Support
-
Build dashboards, automated reports, and data stories to communicate findings to stakeholders at all levels.
-
Provide data-driven recommendations that influence product strategy, operations, marketing, and customer experience.
Research and Innovation
-
Stay current with advancements in machine learning, statistics, and data engineering, and proactively integrate new approaches.
-
Identify opportunities for novel applications of AI/ML to existing or emerging business challenges.
Preferred Qualifications:
-
Required
-
Bachelor’s or Master’s degree in a quantitative discipline such as Statistics, Computer Science, Mathematics, Engineering, Economics, or Physics.
-
3–5+ years of experience in applied data science roles, with a strong record of delivering production-grade models or business-critical insights.
-
Proficiency in Python (pandas, scikit-learn, NumPy, statsmodels) or R.
-
Strong SQL skills and comfort working with relational and non-relational databases.
-
Deep understanding of statistical theory, machine learning fundamentals, and data visualization best practices.
-
Experience using business intelligence tools such as Tableau, Looker, or equivalent.
-
Ability to clearly explain technical topics to non-technical audiences and influence stakeholders with data.
Preferred
-
Ph.D. in a quantitative field with applied research experience.
-
Experience working in cloud environments (AWS, GCP, Azure) and with distributed computing frameworks (e.g., Spark, Dask).
-
Familiarity with MLOps and model deployment pipelines.
-
Experience in time-series modeling, natural language processing, or deep learning.
-
Prior experience mentoring junior data scientists or leading technical projects.
-
