Hire Data Scientists & ML Engineers

A data analyst focuses on describing what happened — aggregating, visualising, and reporting on historical data to answer defined business questions. A data scientist focuses on predicting what will happen — building statistical and machine learning models that generate forward-looking insights. The line blurs in practice, but the key distinction is model-building versus reporting. Kultrix data scientists are comfortable in both roles.

Standard start time is 48 hours from contract signing. Data scientists with specific ML domain expertise — NLP, computer vision, time-series forecasting, or deep learning — may take up to one week to source from our bench. We match the specialist profile to your actual use case rather than assigning a generalist.

Our data scientists are comfortable with cloud data warehouses (Snowflake, BigQuery, Redshift), pipeline tools (dbt, Airflow, Prefect), and ML platforms (AWS SageMaker, Google Vertex AI, Azure ML). We assess your existing data stack during scoping and recommend tooling that integrates with what you already have rather than introducing unnecessary new infrastructure.

Yes — messy data is the normal starting point, not the exception. Our data scientists allocate significant time to data profiling, cleaning, and feature engineering before modelling. We document data quality issues transparently, flag which issues need upstream fixes versus which can be addressed in the pipeline, and build models that degrade gracefully when input quality varies.

We use appropriate evaluation metrics for each problem type — precision/recall/AUC for classification, RMSE/MAE for regression, nDCG for ranking. We apply cross-validation to avoid overfitting on the training set, hold out a temporal test set when data is time-ordered, and compare model performance against a sensible baseline (e.g., always predict the majority class, recommend the most popular item). We document methodology so results are reproducible.

Yes. We containerise trained models as REST APIs with FastAPI or BentoML, deploy to cloud infrastructure (AWS ECS, GCP Cloud Run, or Kubernetes), set up prediction logging, and configure monitoring for data drift and prediction distribution shift. We treat ML model deployment as a software engineering problem — with CI/CD, testing, and observability — not a one-time notebook export.

scikit-learn for classical ML (gradient boosting, regression, clustering). TensorFlow and PyTorch for deep learning. Hugging Face Transformers for NLP and LLM fine-tuning. LightGBM and XGBoost for tabular data. Prophet and statsmodels for time-series. We choose the framework appropriate for the problem and scale rather than defaulting to deep learning for tasks that classical methods solve more efficiently.

Yes. Model interpretability and communication is a core skill. We use SHAP values and LIME for local and global feature importance, build business-facing dashboards that translate model outputs into actionable metrics, and write executive summaries that describe model behaviour in terms of business outcomes — not statistical abstractions. A model no one understands does not get used.

Yes. Kultrix data scientists collaborate with analytics engineers and BI teams — contributing to the data warehouse modelling layer in dbt, aligning on metric definitions, and sharing feature stores across ML and BI use cases. We avoid creating shadow data pipelines that diverge from the canonical analytics layer.

Both. Batch prediction systems — running overnight scoring jobs and writing predictions to a database for dashboards or email campaigns — are simpler to build and maintain and are appropriate for most use cases. Real-time prediction systems — serving predictions via API during a user session — are required for features like fraud detection, personalisation, and search ranking. We recommend the right architecture for your latency and throughput requirements.

We audit training data for demographic skew, measure model performance across protected sub-groups, and apply fairness constraints or post-processing corrections when performance disparities are unacceptable. For products with regulatory sensitivity (lending, hiring, healthcare), we document model cards with bias audit results and flag when a use case requires legal review before deployment.

We offer project-based engagements (defined deliverable with a timeline and fixed or T&M price) and monthly retainer engagements (ongoing data science capacity for continuous model development and maintenance). Both include weekly reporting and a dedicated account manager. Minimum engagement is one month; most ML projects run two to four months from data assessment to production deployment.