Analyzing and interpreting data; Study Design and Methods. [16]

Data Scientist

What this job does:

Use advanced statistics and machine learning to answer high-impact questions, build predictive models, and create data products that improve decisions (product, engineering, R&D, or clinical).

This job is similar to these lab/research tasks:

Modeling: fit models, compare baselines, error analysis; Pipelines: build reusable scripts, manage data/metadata, automate preprocessing; Evaluation: cross-validation, ablations, power checks, negative controls; Stakeholders: write clear result summaries, present to lab/PI, iterate on questions; Other: document methods, code review/mentoring, ensure reproducibility.

Typical starting salary range

$121k to $197k

Salary estimate represented as 25th to 75th percentile. Gathered from recent job postings, Glassdoor.com, and Salary.com

Daily task breakdown

Model development & statistical/ML analysis 35%
Data wrangling, feature engineering & pipelines 20%
Experiment design, evaluation & validation 15%
Stakeholder communication & decision support 20%
Other (mentoring, documentation, governance) 10%.

Skills you'll use in this job:

Python (plus SQL) and strong statistics; ML modeling and evaluation; experiment design/A-B testing or causal inference; data visualization/storytelling; data engineering basics (pipelines, versioning, Continuous Integration/Continuous Deployment [CI/CD]) and domain knowledge.

Talk about these PhD skills in your resume and interview:

Frame your PhD as end-to-end model development: problem formulation, data collection/cleaning, method selection, validation, and communicating implications. Mention reproducible pipelines (Git, tests), preregistered analyses or power calculations, and translating findings into decisions.