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Relocation Package Data Science Jobs in Tulsa, OK

CCT

Machine Learning Engineer (AWS)

Tulsa, OK · On-site +1

... and packaging through deployment, monitoring, retraining, security, and cost control. This role ... You'll partner closely with data scientists, engineers, and product stakeholders to turn complex ...

CCT

Machine Learning Engineer (AWS)

Tulsa, OK

... and packaging through deployment, monitoring, retraining, security, and cost control. This role ... You'll partner closely with data scientists, engineers, and product stakeholders to turn complex ...

Source Medical, LLC.

Urology Physician

Tulsa, OK · On-site

... on bonus, relocation assistance, and resident/fellow stipend for qualifying candidates ... package including CME allowance and loan repayment optionsWhy? Leading not-for-profit healthcare ...

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All JobsRelocation Package Data Science JobsRelocation Package Data Science Jobs in Tulsa, OK

Relocation Package Data Science information

See Tulsa, OK salary details

$34.3K

$112.1K

$179.5K

How much do relocation package data science jobs pay per year?

As of Jun 27, 2026, the average yearly pay for relocation package data science in Tulsa, OK is $112,106.00, according to ZipRecruiter salary data. Most workers in this role earn between $90,000.00 and $124,200.00 per year, depending on experience, location, and employer.

What is a relocation package for data science jobs?

A relocation package for data science jobs is a set of benefits provided by employers to help new hires move to a new location for work. These packages typically cover expenses such as moving costs, temporary housing, travel expenses, and sometimes assistance with finding permanent housing. The goal is to reduce the financial and logistical burden of relocating so that data scientists can start their new roles smoothly. The specifics of a relocation package can vary widely between companies, locations, and job levels.

How does a typical relocation package support data science professionals moving to a new city or country for a role?

A typical relocation package for data science professionals often includes assistance with moving expenses, temporary housing, and support with visa or work permit processes. You may also receive help with finding permanent accommodation and settling-in services, such as local orientation or language courses. These benefits are designed to ease the transition so you can focus on your new role, collaborate effectively with your team, and integrate quickly into the organization. Relocation packages vary by company, so it's a good idea to clarify the details during the hiring process.

What are the key skills and qualifications needed to thrive as a Data Scientist, especially when utilizing relocation packages, and why are they important?

To thrive as a Data Scientist, you need strong analytical skills, proficiency in statistics, programming (typically Python or R), and a relevant degree such as in computer science or mathematics. Experience with machine learning frameworks, data visualization tools, and familiarity with cloud platforms and big data systems are commonly required, while certifications like AWS Certified Data Analytics or Google Data Engineer can be advantageous. Excellent problem-solving, communication, and adaptability skills help you collaborate across diverse teams and adjust to new environments, especially during relocation. These skills ensure you can extract actionable insights from complex data, integrate smoothly into new workplaces, and drive impactful business decisions.

What is the difference between Relocation Package Data Science vs Data Analyst?

AspectRelocation Package Data ScienceData Analyst
Required CredentialsBachelor's or Master's in Data Science, Computer Science, or related fieldsBachelor's degree in Statistics, Mathematics, or related fields
Work EnvironmentTech companies, consulting firms, or finance sectors with complex data projectsBusiness, marketing, or finance departments analyzing data for insights
Employer & Industry UsageCommon in industries offering relocation packages for specialized rolesWidely used across industries for routine data analysis tasks

Relocation Package Data Science roles typically require advanced degrees and focus on developing predictive models and algorithms, often in tech or finance sectors. Data Analysts usually have similar educational backgrounds but focus on interpreting data and generating reports. Both roles may involve relocation, but Data Science positions tend to be more specialized and technical.

What are popular job titles related to Relocation Package Data Science jobs in Tulsa, OK? For Relocation Package Data Science jobs in Tulsa, OK, the most frequently searched job titles are:
What job categories do people searching Relocation Package Data Science jobs in Tulsa, OK look for? The top searched job categories for Relocation Package Data Science jobs in Tulsa, OK are:
Relocation Package Data Science jobs near you
Machine Learning Engineer (AWS)

Machine Learning Engineer (AWS)

CCT

Tulsa, OK • On-site, Remote

Full-time

Posted 27 days ago

Job description

Summary
We're looking for a Machine Learning Engineer to design, deploy, and operate production ML systems on Amazon Web Services. You'll own the full lifecycle in a real-world, high-stakes environment - from training and packaging through deployment, monitoring, retraining, security, and cost control.
This role sits at the intersection of ML engineering and MLOps and is core to CCT's analytics strategy. You'll partner closely with data scientists, engineers, and product stakeholders to turn complex time-series and transactional data into reliable, observable, and cost-effective ML services that our customers can trust.
You'll thrive here if you naturally dig into why models behave the way they do, enjoy tracing issues to their root cause, and like collaborating across disciplines to ship robust systems that are built to last.
What You'll Do
  • Build and maintain reproducible model training workflows on AWS (SageMaker, S3, Glue, etc.), making retraining, rollback, and experimentation routine rather than heroic.
  • Deploy and operate real-time and batch inference services with full CI/CD pipelines, versioning, and safe rollout strategies (canary, shadow, A/B) so changes are deliberate and observable.
  • Instrument production models for performance, data drift, latency, and errors - and automate retraining triggers when models drift out of tolerance.
  • Maintain model lineage, auditability, and traceability to meet the compliance, governance, and reporting needs of the regulated gaming industry.
  • Enforce least-privilege IAM, encryption, and secure data access patterns across the entire ML platform.
  • Treat cost as a first-class engineering metric - right-size infrastructure, balance batch vs. real-time workloads, and continually reduce platform spend without sacrificing reliability.
  • Collaborate with engineers, data scientists, and product teams to translate business problems into ML solutions, communicate tradeoffs clearly, and iterate based on feedback.
  • Continuously explore new AWS services, ML frameworks, and deployment patterns to improve reliability, observability, and developer velocity on the ML platform.

Requirements
  • 3+ years of experience in machine learning engineering, MLOps, or a closely related discipline.
  • Hands-on experience with AWS ML and data services - SageMaker (training, endpoints, pipelines), S3, Lambda, Step Functions, CloudWatch, MWAA (Apache Airflow).
  • Experience working with time series data, including feature engineering, seasonality handling, and temporal train/test splits.
  • Strong Python skills and familiarity with common ML frameworks (scikit-learn, PyTorch, XGBoost, or equivalent).
  • Experience building and maintaining CI/CD pipelines for ML systems.
  • Demonstrated ability to monitor and debug production ML systems - latency, drift, errors, and data quality - and drive issues to root cause.
  • Comfort with SQL and working with structured data at scale.
  • Able to work collaboratively across teams, assume positive intent, and communicate clearly with both technical and non-technical stakeholders.
  • Track record of self-directed learning and technical growth in areas like AWS, ML frameworks, or deployment patterns.

Certified Banana Picker
Nice to Have
  • Experience in a regulated industry (gaming, finance, healthcare) where auditability, explainability, and compliance are first-class concerns.
  • Familiarity with feature stores, model registries, or ML metadata tools (e.g., MLflow, SageMaker Model Registry).
  • Experience with infrastructure-as-code (Terraform, CDK, or CloudFormation).
  • Exposure to data drift detection libraries or custom drift monitoring implementations.

Success Looks Like
  • Production models run reliably with clear, measurable business impact for casino operators.
  • Failures are observable, recoverable, and explainable - with logs, metrics, and traces that tell the full story.
  • ML systems scale predictably with usage and data volume, without runaway cost.
  • The ML platform becomes a trusted, well-understood part of CCT's product ecosystem - for both internal teams and external customers.

About CCT
CCT is the creator of Casino Insight™, the award-winning platform trusted by more than 350 casinos worldwide to automate cage operations, revenue audits, and operational analysis. Since 2012, Casino Insight has helped casinos replace manual work with streamlined workflows, improving accuracy, compliance, and profitability.
Headquartered in Tulsa, Oklahoma, CCT integrates seamlessly with leading casino management, hospitality, and financial systems-delivering measurable ROI and empowering teams to work smarter at every level.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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