Note: The job is a remote job and is open to candidates in USA. ActivTrak is seeking a Senior Data Scientist to build and own production machine learning for its workforce intelligence products. The role covers problem formulation, feature definition, model and scoring logic, evaluation, deployment collaboration, and ongoing monitoring of model performance for behavioral event data, including role prediction, activity classification, and cross-account benchmarking.
Responsibilities
- 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
- You own problem formulation, feature definition, model and scoring logic, evaluation, and ongoing model performance in production
- 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
Skills
- 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
- Position is remote within US
- Minimal travel
- Limited physical demands
- 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
- Position is remote within US
- Minimal travel
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