Senior Data Scientist

ActivTrak; Austin, Northern (TX, KY); Hybrid; USD 130,000 - 170,000; Full time

Job summary

ActivTrak is expanding its Data Science team to ship production machine learning powering our workforce intelligence products. This hands-on role goes from problem formulation through a shipped, monitored model, not just notebooks.

You will own feature definition, model logic, and ongoing production performance, collaborating with Data Engineers to ensure durable deployment and reliability.

Qualifications

• 5+ years bringing machine learning to production at scale with production ownership.
• Strong Python and production-grade software engineering practices.
• Experience with classification/prediction on behavioral event data and robust feature engineering.
• Strong SQL skills and direct work against production data sources.
• Experience monitoring model quality after deployment and handling drift.

Responsibilities

• Own problem formulation, feature definition, model and scoring logic, evaluation, and production performance.
• Collaborate with Data Engineers for durable, scalable deployment and reliability.
• Maintain ownership of the model delivering the intended outcomes and iterating over time.
• Operate as an individual contributor with no people-management responsibilities.

Skills

Production ML

Python

SQL

Feature engineering

Model monitoring

Testing & version control

Git

Tools

Docker

Kubernetes

Job description

We're growing our Data Science team to ship production machine learning that powers ActivTrak's next generation of workforce intelligence products. This is a hands‑on role built around production ownership: you take a problem from formulation through a shipped, monitored model, not just to a notebook.

You’ll work on problems like:

• Predicting user roles and classifying activity from behavioral event data
• Cross-account benchmarking that turns aggregate usage patterns into product-differentiating insight
• Making pragmatic tradeoffs between model sophistication, business value, reliability, latency/cost, and iteration speed
• Partnering with our Data Engineers, who take your models and analysis and turn them into durable, scalable production systems

Where this role starts and ends:

You own problem formulation, feature definition, model and scoring logic, evaluation, and ongoing model performance in production. Data Engineering owns the pipeline infrastructure, orchestration, deployment mechanisms, and operational reliability that put your work into production. The primary ownership is clear, but you'll work together across that boundary when production issues span model and platform - your job is the model delivering the intended outcome and improving over time.

This is an individual-contributor role with substantial ownership over your problem space. It does not include people‑management responsibilities.

Must‑Haves:

• 5+ years bringing machine learning to production at scale, with direct experience making the sophistication/speed/reliability tradeoffs described above
• Strong Python and production‑grade software engineering practices (testing, code review, version control)
• Experience with classification/prediction problems on behavioral, event, or user activity data
• Strong SQL and comfort working directly against production data sources, not just flat files or CSVs
• Experience with feature engineering: defining, standardizing, and validating features for production models
• Experience monitoring model quality after deployment and responding to drift or degradation, not just shipping and moving on

Nice‑to‑Haves:

• Time series analysis and/or hidden state models
• Parallel dataframes (Dask, Spark, or similar)
• Comfort working within a layered/medallion-style data architecture (raw → cleansed/identified → aggregated/de‑identified)
• Feature store experience (versioning, storage, reuse)
• Cloud environment experience (GCP or AWS), and general comfort operating around containerized/orchestrated infrastructure (Docker, Kubernetes) even if you're not the one building it

Why Should You Apply?

• Own production ML that reaches customers and improves through real‑world feedback
• Work on a genuinely uncommon ML problem: behavioral event data from 9,500+ customer organizations, used for role prediction, activity classification, and cross‑account benchmarking
• Small, senior team with real ownership and visibility to leadership

Work environment

• Position is remote within US
• Minimal travel
• Limited physical demands

This is an incredible opportunity to embark on an exciting journey with a dynamic, VC-backed company. If you have a proven track record of creative thinking, a drive for learning, and a deep commitment to collaboration, we want to talk to you!

ActivTrak is an equal opportunity employer.

We celebrate diversity and are committed to creating an inclusive environment for all employees.

ActivTrak does not discriminate on the basis of race, color, religion, sex, national origin, political affiliation, sexual orientation, marital status, disability, age, protected veteran status, gender identity or any other factor protected by applicable federal, state or local laws.