Hire in days, not months

Hire Data Scientists

Drive data-driven product decisions with senior data scientists who focus on actionable insights.

From predictive modeling to complex NLP workflows, our data scientists help you extract value from data, improve user experiences, and keep product iteration predictable and evidence-based.

Get Matched with Data Science Talent

Data science delivery governance

Governance built for accurate and reliable data insights

Reduce analytical risk with explicit evaluation controls, bias checks, and performance monitoring tailored to data science projects.

Controls teams ask for before data launch

Accuracy, security, and bias discipline mapped to how modern data science projects actually ship.

Shortlist turnaround

4.1 days median across recent data science roles

Kickoff speed

10 days median from selection to sprint start

Insight reliability

95% of data models meet quality benchmarks after 90 days

Model evaluation and bias guardrails

Automated testing for model accuracy, fairness, and bias to ensure data insights are reliable and ethical.

Quality control

Data privacy and security compliance

Secure handling of PII, encrypted data processing, and compliance with data residency requirements for data science workloads.

Security-ready

Model and health monitoring

Real-time tracking of model performance, data drift, and user-perceived value in production.

Health-focused

Talent pool preview

Vetted Data Scientist profiles ready to interview

Review a balanced shortlist with specialist, senior, and principal depth so you can hire for immediate delivery and long-term technical leadership.

View more profiles

Ali P.

Senior Data Scientist

Vetted

8 years

Role-matched

PythonMachine LearningA/B TestingPandas

Led the data science initiatives for a high-traffic SaaS platform, implementing predictive churn models that reduced user attrition by 20%.

Ahsan E.

Data Scientist

Vetted

6 years

Role-matched

PythonNLPRecommendation SystemsSQL

Developed a personalized recommendation engine for a large ecommerce site, increasing average order value by 15% through better product discovery.

Muhammad T.

Principal Data Scientist

VettedArchitect

12 years

Role-matched

Deep LearningMLOpsStatistical ModelingSystem Design

Architected a secure and scalable ML platform for a fintech firm, improving fraud detection accuracy by 35% while ensuring strict data privacy.

Need a wider shortlist?

We can share additional data scientist profiles by seniority, timezone, and domain fit.

Data science engagement options

Choose the engagement model that matches your data roadmap

Start with focused analytical work or scale to a full data pod as your product complexity grows.

Model selection support

We map data science role shape to roadmap pressure, technical complexity, and stakeholder expectations.

Part-time data science support

Best for iterative analytical work, model optimization, and ongoing maintenance.

Starts from $2,000 / month

Best for: Steady analytical improvements and maintenance

20-25 hrs/week
Data sprint support
Weekly accuracy reporting

Large-scale data engineering and model training are scoped separately.

Get Started

Full-time data scientist

Recommended

Best for core data feature delivery with daily ownership and production momentum.

Starts from $4,000 / month ($25/hour)

Best for: Active data roadmap execution and product integration

40 hrs/week
Full ownership
Daily accuracy + progress updates

Cloud data platform costs and third-party licensing are billed separately.

Get Started

Data science pod (2 Data Scientists + 1 Data Eng + 1 PM)

Best for new product launches, major analytical sets, and cross-functional execution.

Starts from $12,000 / month

Best for: High-stakes initiatives with significant coordination needs

Cross-functional data pod
Parallel workstreams
End-to-end orchestration

Specialized security audits are scoped separately.

Get Started

Data science hiring process

From data roadmap to production contribution in under two weeks

The process is tuned for data science delivery risk: model fit, analytical depth, and release reliability.

Typical kickoff window

Most teams start data science delivery with selected talent in 7-14 days.

Decision points are explicit: data implementation depth, analytical discipline, and communication quality are validated before kickoff.

1
Data goal alignment and scope
Step 1
We map your data objectives, technical requirements, and business goals to define role scope and success metrics.
Day 1-2
2
Shortlist with relevant data context
Step 2
Review candidates with prior experience in similar data domains, analytical patterns, or scale constraints.
Day 2-5
3
Technical validation with data scenarios
Step 3
Interviews test data science logic, analytical depth, and delivery-specific tradeoff handling.
Day 5-10
4
Onboarding and data sprint integration
Step 4
Selected scientists join your workflows with clear ownership and immediate first-sprint goals.
Day 7-14

Why product teams hire us for data science

Data science execution tuned for accuracy, speed, and actionable insights

You get scientists who can build production-grade analytical systems without the overhead of a traditional data team.

Built for high-stakes data delivery

Designed for teams shipping SaaS products, ecommerce tools, and performance-critical data experiences.

Typical start

10 days median to sprint kickoff

Accuracy lift

28% median improvement in model accuracy

Insight speed

35% increase in data-driven decision frequency

Fast ramp on data science projects

Scientists integrate into your architecture, analytical patterns, and release flow quickly.

Velocity

Focus on analytical accuracy and reliability

Scientists prioritize evaluation and validation to ensure data insights are safe for production.

Reliability

Actionable data-driven performance

Delivery decisions account for scale, speed, and user-perceived value.

Performance

Service scope

Data science use cases mapped to business outcomes, not just algorithms

Use this service scope to match your data roadmap to the right analytical pattern, whether you need predictive insights, user personalization, or scalable ML infrastructure.

Machine Learning and Modeling

Predictive modeling and forecasting

Our data scientists build robust predictive models for churn reduction, demand forecasting, and user behavior analysis to drive proactive business decisions.

NLP and text analytics

Hire data experts to develop advanced NLP solutions for sentiment analysis, automated categorization, and intelligent content extraction from unstructured data.

Recommendation engines and personalization

Design and implement scalable recommendation systems that improve user engagement and conversion through personalized content and product discovery.

Analytics and Insights

Advanced statistical analysis and A/B testing

Design and analyze rigorous A/B tests and experiments to validate product changes and ensure data-driven growth strategies.

Data visualization and dashboarding

Create intuitive, interactive dashboards using Tableau, PowerBI, or custom tools to communicate complex data insights to stakeholders clearly.

Customer segmentation and life-time value (LTV)

Identify high-value user segments and predict customer lifetime value to optimize marketing spend and product focus.

Data Strategy and MLOps

MLOps and model deployment

Hire data scientists to automate the deployment, monitoring, and retraining of ML models, ensuring reliable performance in production.

Data strategy and roadmap planning

Collaborate with stakeholders to define data objectives, identify high-impact use cases, and build a scalable data science roadmap.

Feature engineering and data preparation

Design and implement efficient feature engineering pipelines that improve model accuracy and reduce data processing latency.

Engineering stack

Production-ready data science stack for accuracy, speed, and actionable insights

Stack choices are optimized for fast analytical iteration, model reliability, and scalable data processing across modern data products.

Python

SQL

Jupyter

Scikit-learn

PyTorch

TensorFlow

XGBoost

Pandas

NumPy

Apache Spark

Dask

Tableau

PowerBI

Matplotlib

Seaborn

Plotly

MLflow

AWS SageMaker

GCP (Vertex AI)

Docker

Hiring readiness

Data science hiring playbook: evaluate quickly and onboard with less risk

Use this decision hub to align data interview depth, set quality boundaries, and connect hiring to measurable outcomes.

Role scope Interview playbook Business impact

Responsibilities / Role Scope

Owns

ML model development and evaluation with high accuracy standards
Data analysis and insight generation for product decisions
A/B testing design and statistical validation
Data visualization and stakeholder communication

Collaborates on

Product teams to define data objectives and feature feasibility
Data engineers for efficient data access and pipeline design
DevOps teams for secure model deployment and monitoring
Marketing teams for user segmentation and LTV optimization

Interview Questions

Structured by level for consistent and faster interviewer calibration.

junior

Fundamentals and execution reliability

What is the difference between supervised and unsupervised learning?
How do you handle missing values in a dataset?
What is overfitting and how do you prevent it?
How do you explain a complex model's prediction to a non-technical stakeholder?

mid

Delivery ownership and decision quality

How do you choose the right evaluation metric for a classification problem?
What techniques do you use for feature selection and engineering?
How do you design and analyze an A/B test for a new product feature?
How do you handle imbalanced datasets in machine learning?
When would you use a random forest versus a gradient boosting model?

senior

Architecture, risk control, and leadership

How do you architect a scalable ML pipeline for real-time predictions?
How do you manage model drift and ensure long-term model performance in production?
How do you approach a complex data science problem with limited or noisy data?
How do you define and communicate the ROI of data science initiatives to leadership?
How would you design a multi-objective recommendation system for a large marketplace?

Why Outsource This Role

Faster data-driven decisions

Extract actionable insights from your data without the overhead of local hiring or complex analytical research.

Median kickoff: 10 days from role approval

Predictable analytical costs

Scale your data science bandwidth based on active priorities at a predictable hourly rate.

Starts from $25/hour for data science

Improved model accuracy

Reduce errors and improve prediction relevance with scientists who know ML tradeoffs.

Median accuracy lift: 28% in 12 weeks

Lower analytical risk

Use data science best practices and rigorous validation to reduce biased or inaccurate insights.

Model drift incidents reduced 30% quarter-over-quarter

Scalable data science teams

Start with one data scientist and expand to a full data pod as product complexity grows.

Data pod scale-up window: 2-3 weeks

Benefit	Outcome	Metric
Faster data-driven decisions	Extract actionable insights from your data without the overhead of local hiring or complex analytical research.	Median kickoff: 10 days from role approval
Predictable analytical costs	Scale your data science bandwidth based on active priorities at a predictable hourly rate.	Starts from $25/hour for data science
Improved model accuracy	Reduce errors and improve prediction relevance with scientists who know ML tradeoffs.	Median accuracy lift: 28% in 12 weeks
Lower analytical risk	Use data science best practices and rigorous validation to reduce biased or inaccurate insights.	Model drift incidents reduced 30% quarter-over-quarter
Scalable data science teams	Start with one data scientist and expand to a full data pod as product complexity grows.	Data pod scale-up window: 2-3 weeks

Client stories

Trusted by teams that ship fast

Real feedback from partnerships where we embedded with product teams, accelerated delivery, and stayed accountable to outcomes.

“We needed to launch a new product line on a fixed deadline, and missing it would have impacted revenue. Codexty helped us reorganize delivery, close technical gaps, and execute with steady weekly progress updates. We shipped ahead of schedule and exceeded our initial activation targets in the first month.”

Lisa R.

Operations Director, Logistics Company

“Onboarding was fast and structured, which gave us confidence from day one. The engineer asked sharp questions, aligned on priorities quickly, and maintained consistent velocity across every sprint. By month two, they were owning critical tickets independently and mentoring junior members of our team.”

James H.

CEO, AI-first Startup

“What stood out was how quickly they understood both our codebase and business constraints. Their developer contributed meaningful pull requests in week one, improved our testing discipline, and proactively flagged architecture risks before they became expensive problems. It felt less like hiring a contractor and more like adding a senior teammate.”

Elena M.

VP Engineering, Fintech Platform

FAQ

Answers to practical decision questions before you hire.

How quickly can a data scientist start?

Most data science projects begin onboarding within 7-14 days after role alignment and interview completion.

Do you work with Python and Machine Learning?

Yes. We specialize in modern data science using Python, Machine Learning, NLP, and various cloud-native tools.

Can you help with A/B testing design?

Yes. We support designing and analyzing rigorous A/B tests to validate product changes and ensure data-driven growth.

How do you handle model drift?

We implement model monitoring and automated retraining to ensure your insights stay fresh and performance stays predictable.

What is the hourly rate for data scientists?

Our data science services start at $25/hour, providing high-quality analytical delivery at a competitive rate.