Practice · Data Science
Applied Data Science & Machine Learning
Most data science work stalls in a notebook. Althrix builds production models tied to a business decision — forecasting, churn, pricing, anomaly detection — with the pipelines, monitoring, and retraining that keep them useful in month six.
Problems we solve
- • Forecasts are gut-feel spreadsheets that miss by 20–40%
- • Churn and lead-scoring models never made it out of the notebook
- • Anomalies in operations or transactions are caught after the fact
- • Pricing and promotion decisions ignore elasticity data
- • There's no MLOps: no monitoring, no retraining, no ownership
What we build
- → Demand, revenue, and cash-flow forecasting models
- → Churn, propensity, and lead-scoring models with clear thresholds
- → Anomaly and fraud detection on transaction and telemetry streams
- → Pricing, elasticity, and promo-lift models
- → Recommendation and next-best-action engines
- → MLOps: feature stores, monitoring, retraining, drift alerts
Stack we work with
Python (pandas, scikit-learn, PyTorch, XGBoost, Prophet), dbt, Airflow / Dagster, Snowflake / BigQuery / Postgres, MLflow, and serving via FastAPI, Modal, or SageMaker.
Have a decision that deserves a model?
Tell us the decision and the data you already have — we'll come back with a scoped, production-oriented plan.
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