Data Science Staffing | ML Engineers & Data Platform | ApTask

Data science · Analytics · ML · Data engineering

Data scientists, ML engineers, analytics engineers, and data platform leads — screened on production deployment history, not just notebook experiments or Kaggle competition rank.

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Why ApTask for data science staffing

What separates a data science staffing partner from a vendor.

Production over experimentation.

A model that never reached production is a research project. We screen for engineers who have owned model deployment, monitoring, retraining cadence, and the inevitable failure modes — the engineers your roadmap actually needs.

Full-stack data, not just data science.

We staff the whole pipeline: data engineers (Spark, dbt, Airflow, Dagster), analytics engineers (Looker, Tableau, semantic layer), ML engineers (TensorFlow, PyTorch, MLflow, Vertex AI, SageMaker), and applied scientists. Pairing across the stack is what gets models shipped.

Modern data stack fluent.

Snowflake, Databricks, BigQuery, Redshift. dbt, Fivetran, Airbyte. Looker, Mode, Hex, Sigma. Our recruiters operate against the actual modern data stack — not against decade-old "Hadoop developer" requisition templates.

Domain alignment.

A retail data scientist screening for a healthcare role is going to need a steep ramp. We match candidates against the specific vertical you operate in — retail, financial services, healthcare, logistics — so the domain knowledge transfers on day one.

What we screen for

Authenticated before submission.

Our data-science recruiters hold quantitative, analytics, or data-engineering backgrounds themselves. Screening closes before submission and includes:

Live technical interview against the candidate’s claimed stack
Reference project review — specific models the candidate shipped to production
Statistical fluency check (frequentist + Bayesian, causal inference depth)
ML system design walkthrough for senior engineering roles
SQL proficiency screen (window functions, query optimisation, set theory)
Cloud ML platform certification verification (SageMaker, Vertex AI, Azure ML)
Data engineering depth (Spark, Airflow, dbt) for full-stack roles
Domain-vertical alignment review

Engagement-model fit

Data science engagements typically run through:

Permanent data-science hires and senior contract engineers engage through Strategic Workforce Staffing. For defined-outcome work — a recommender system build-out, a forecasting platform, a customer-data-platform implementation — Managed Solutions (SOW) is the right fit. Global teams pair through our EOR practice.

Read about Strategic Workforce Staffing

Quantified outcomes

Engagements that moved the metric.

4 weeks

Recommender ship · Fortune 100 retailer

Four-engineer team delivered a personalisation recommender against the client’s product catalog. Drove an 11% lift in attach rate on the launch cohort, sustained through the holiday quarter.

70%

Forecasting accuracy improvement · global logistics

Replaced a legacy ARIMA-based forecasting system with a modern ML pipeline. Reduced inventory carry costs by an estimated $4.2M annualised.

60+

Data scientists and ML engineers in active bench

Across Python, R, SQL, Spark, and the major cloud ML platforms — calibrated against retail, financial services, healthcare, and logistics verticals.

Common questions

Buyer questions, answered.

What data and analytics roles does ApTask staff?

Data scientists (mid through staff/principal), ML engineers, applied scientists, data engineers (Spark, dbt, Airflow, Dagster), analytics engineers (Looker, dbt, semantic layer), MLOps engineers, data platform engineers (Snowflake, Databricks, BigQuery), and analytics leadership (managers and heads-of).

How does ApTask validate that a candidate has shipped models to production?

Reference-project review with the candidate’s prior managers, specifically probing on production deployment cadence, model monitoring, retraining processes, and incident handling. A candidate who can articulate a production failure they recovered from is a much stronger signal than one whose résumé reads like a Kaggle leaderboard.

Can ApTask deliver a data platform build-out as a managed solution?

Yes — through Managed Solutions (SOW). Reference engagements include modern data stack migrations (legacy on-prem to Snowflake/dbt), CDP implementations, and forecasting/recommender platform builds.

Does ApTask staff for healthcare or financial-services data science specifically?

Yes — both. Healthcare engagements clear HIPAA-aware screening; financial services engagements clear model-governance and SR 11-7 fluency where required. Domain alignment is part of intake.

How does ApTask source data science candidates?

A combination of our 2.1M+ verified network, conference-and-community presence (NeurIPS, ICML, Strata, MLOps Community), and active sourcing through the specific domain communities relevant to the role. Passive candidates make up the majority of our senior placements.

Talk to a strategist about your data science staffing need.

Send us the role, the timeline, and the constraint you’re most stuck on. A vertical recruiter will respond inside 24 hours with a calibration slate and a written staffing thesis.

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