Hire Remote Data Scientists

Hire Data Scientists Who Ship Models to Production — Not Just Notebooks

The gap between a promising Jupyter notebook and a production ML model that drives business value is where most data science initiatives fail. Data Scientists who can take a model from exploration through feature engineering, validation, deployment, and monitoring — and who measure success in business outcomes rather than model accuracy scores — are the ones who actually deliver ROI.

We match you with senior Data Scientists who’ve shipped production ML models for e-commerce, fintech, healthtech, and SaaS companies. Engineers who combine rigorous statistical foundations with the software engineering practices that take models from research to revenue-generating production systems.

Start in days, not months. Pay 50% less than equivalent US-based data science talent.

What Our Data Scientists Build

Predictive & Recommendation Systems

Customer churn prediction, next-best-action models, product recommendation engines, lead scoring, demand forecasting, and personalization systems — production ML that drives measurable revenue and retention improvement.

NLP & Text Analytics

Sentiment analysis, document classification, named entity recognition, semantic search, topic modeling, and LLM fine-tuning for domain-specific applications. Text-based systems that turn unstructured content into structured business intelligence.

Anomaly Detection & Risk Models

Fraud detection, operational anomaly detection, credit risk scoring, and predictive maintenance models. Statistical and ML approaches to catching problems before they become costly.

Experimentation & A/B Testing

Experiment design, statistical power analysis, multi-armed bandit optimization, and Bayesian A/B testing frameworks that make product decisions data-driven rather than opinion-driven.

MLOps & Model Production

Feature stores, model registries, model serving infrastructure (Seldon, BentoML, SageMaker endpoints), monitoring for data drift and model degradation, and the retraining pipelines that keep models current.

Data Science Technology Stack

Languages: Python (primary), R, SQL, Scala

ML Frameworks: scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, Hugging Face

Data Processing: Pandas, Polars, PySpark, Dask, DBT

MLOps: MLflow, Weights & Biases, SageMaker, Vertex AI, Databricks, Kubeflow

Databases: PostgreSQL, BigQuery, Snowflake, Redshift, DynamoDB, Elasticsearch

Visualization: Matplotlib, Plotly, Tableau, Looker, Streamlit (model demos)

Client Success Story: E-Commerce — Recommendation Engine Drives $8M in Incremental Revenue

A mid-market online retailer’s product recommendations were powered by basic “customers also bought” rules — no personalization, no real-time behavioral signals, no model. Our Data Scientist built a two-stage recommendation system: a candidate retrieval model using embedding similarity (ALS collaborative filtering), and a re-ranking model using XGBoost with real-time user session features. Deployed via a SageMaker endpoint integrated into their product API. A/B test over 8 weeks showed 14% improvement in click-through rate and 9% improvement in add-to-cart rate on recommendation placements. Incremental revenue attributed to the model in the first year: $8M.

Client Success Story: SaaS Company — Churn Prediction Saves $2.4M ARR

A Series C SaaS company was losing 4% of ARR monthly to churn — high enough to threaten their growth metrics and raise concerns from investors. Their customer success team was reactive: they only learned about at-risk customers when they submitted a cancellation. Our Data Scientist built a churn prediction model trained on 24 months of behavioral data: login frequency, feature usage depth, support ticket patterns, billing history, and NPS responses. The model generated a weekly at-risk customer list with predicted churn probability and intervention recommendations. Customer success acted on the list and reduced churn by 31% within 6 months. Saved ARR: $2.4M annually.

Why Companies Choose Our Data Scientists

• Production-first: They ship models, not notebooks — production deployment, monitoring, and retraining are part of every engagement
• Business-outcome focus: They measure success in revenue, retention, and efficiency — not model accuracy in isolation
• Full-stack capability: From data exploration through feature engineering, modeling, and MLOps — they own the complete ML lifecycle
• 50% cost savings: Senior data science expertise at a fraction of US market rates
• Fast start: Most engagements begin within 1–2 weeks

Engagement Models

• Individual Data Scientist — One senior data scientist embedded with your team for model development, experimentation, and production ML deployment.
• Data Science + Data Engineering Pod — Data Scientist focused on modeling paired with a Data Engineer handling pipeline and feature infrastructure.
• ML Product Teams — Multiple data scientists and engineers for organizations building ML as a core product capability.
• Contract-to-Hire — Evaluate a data scientist’s modeling approach and business outcome focus before committing long-term.

How To Vet Data Scientists

Our vetting identifies data scientists who ship to production — not just demonstrate model performance in research settings.

1. Problem formulation — Given a business problem (e.g., “we’re losing customers”), how do they formulate it as an ML problem? What data do they need? What would success look like? We look for business problem decomposition, not immediate model selection.
2. Technical depth assessment — Feature engineering, regularization trade-offs, hyperparameter tuning, evaluation metric selection, handling class imbalance, and production considerations. Over 90% of applicants do not pass this stage.
3. Production ML interview — How have they deployed models to production? What monitoring did they implement? How did they detect model drift? What was the retraining strategy?
4. Case study review — Walk us through a production ML project from business problem to production impact. What was the model, what was the result, and what would they do differently?

What to Look for When Hiring Data Scientists

Strong data scientists are applied scientists — they balance rigor with pragmatism to deliver production impact.

What strong candidates demonstrate:

• They start with business problem formulation before discussing model architecture — they know what “success” means before they start modeling
• They’ve deployed models to production and monitored them — they know what data drift looks like and have handled it
• They’re honest about model limitations and failure modes — they set realistic expectations rather than over-promising on accuracy
• They communicate results to non-technical stakeholders in business terms — not just model metrics

Red flags to watch for:

• Portfolio consists entirely of Kaggle competitions and academic papers — no production ML experience
• Maximizes model accuracy without considering latency, explainability, or retraining cost trade-offs
• Can’t explain model results to a business stakeholder without statistical jargon
• No monitoring or drift detection in their production deployments — deploys and forgets

Interview questions that reveal real depth:

• “Walk me through a production ML model you shipped. What was the business problem, what was the model, how did you measure business impact, and what did you learn?”
• “How do you handle a situation where your model performs well on held-out test data but poorly in production?”
• “How would you explain a customer churn prediction model’s output and confidence level to a non-technical customer success manager?”

Related Services

• Data Engineers — Data pipeline engineers who build the data infrastructure data scientists depend on.
• AI Product Engineers — Engineers who productize ML and AI models into scalable product features.
• Generative AI Specialists — Specialists in LLMs, RAG systems, and generative AI application development.
• Data Analysts — Analytics engineers and BI developers who turn data into business intelligence dashboards.

Want to Hire Remote Data Scientists?

We source, vet, and place senior Data Scientists who build production ML systems that drive measurable business outcomes — engineers who go from problem formulation through modeling, deployment, and production monitoring with a focus on business impact, not just model accuracy.

Get matched with Data Scientists →

Ready to hire Data Scientists who ship models to production? Contact us today and we’ll introduce you to senior data scientists within 48 hours.