> ActivTrak · Austin, United States (Remote) · Full-time · Posted 2026-08-26
Workplace: remote
Department: Development
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.
Requirements
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
Benefits
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.
ActivTrak provides the work intelligence organizations need to understand how work changes in the AI era. As the system of record for work, its award-winning platform captures behavioral data across people, tools, and AI agents, and the workflows that connect them — enabling leaders to measure impact, optimize productivity, and improve operational performance. The platform also powers research through the ActivTrak Productivity Lab. Built on a privacy-first data foundation, ActivTrak is trusted by more than 9,500 organizations worldwide and recognized by Deloitte’s Technology Fast 500, Inc. 5000, TrustRadius, and G2 for delivering measurable ROI and stronger business outcomes. The company is backed by Elsewhere Partners, Sapphire Ventures, and Francisco Partners. Learn more at www.activtrak.com.
