Data Scientist – Machine Learning

Quest Global delivers world-class end-to-end engineering solutions by leveraging our deep industry knowledge and digital expertise. By bringing together technologies and industries, alongside the contributions of diverse individuals and their areas of expertise, we are able to solve problems better, faster. This multi-dimensional approach enables us to solve the most critical and large-scale challenges across the aerospace & defense, automotive, energy, hi-tech, healthcare, medical devices, rail and semiconductor industries.

We are looking for humble geniuses, who believe that engineering has the potential to make the impossible possible; innovators, who are not only inspired by technology and innovation, but also perpetually driven to design, develop, and test as a trusted partner for Fortune 500 customers. As a team of remarkably diverse engineers, we recognize that what we are really engineering is a brighter future for us all. If you want to contribute to meaningful work and be part of an organization that truly believes when you win, we all win, and when you fail, we all learn, then we’re eager to hear from you.

The achievers and courageous challenge-crushers we seek, have the following characteristics and skills:

What You will Do:

As a Data Scientist (Machine learning (ML) Ops) you will be part of our customers Digitalization journey, aligning with a broader team to work on challenging projects from all areas of the energy industry. Through combining strategic and business insights with the latest analytical techniques, you will drive maximum value from data and develop analytics solutions that provide strategic advantages in our day-to-day operations. You will transform Machine Learning models into well-engineered products fulfilling development, deployment and monitoring requirements and standards.
● Deploy, operationalize, and maintain machine learning (ML) models with a focus on model hyperparameter optimization, automatic retraining and model training, version control and governance, and model monitoring and drift.

● Establish workflows for model deployment, operations, and decommissioning.

● Track, snapshot, and manage assets used to create models.

● Collaboration, sharing, and standardization of ML pipelines developed by data scientists.

● Maintain the integrity of model assets and logging access controls.

● Certify model behavior to regulatory and adversary standards, supported by data scientists and subject matter experts.

● Support portability of models across a variety of platforms.

● Ensure model performance meets functionality and latency requirements.

● Development, validation, and release of models.

● Evaluate design patterns for model deployment, evaluate design patterns for unit testing and integration testing for machine learning products.

● Build and maintain scalable ML Ops frameworks to support product-specific models.

● Utilize skills and tools to support the WSC Digitalization Journey by building a network of connected machines and digital tools.

What You Will Bring:

● BS/MS in Computer Science/Data Science/Engineering or related field

● Hands-on experience with ML frameworks, libraries, agile environments, and delivering machine learning solutions using DevOps principles. – Required.

● Know-How in data science programming languages (Python, R, Scala) – Required.

● Excellent knowledge of boto3 AWS SDK or additional SDKs for other cloud platforms and good knowledge of cloud infrastructure – Preferred.

● Experience in container technologies (Docker, Kubernetes, etc.) – Preferred.

● Familiar with REST API protocol and at least model serving technologies (MLFlow, Seldon Core, Kubeflow, TFX, Sagemaker endpoints, etc.) – Required

● Excellent knowledge of the ML lifecycle – Preferred.

● Ability in crafting CI/CD pipelines for machine learning projects – Required.

● Experience with serverless computing (Python, Snowflake), knowledge of data warehousing systems, and knowledge of setting up target database systems. – Required.

Pay Range: ($85K - $90K). 

Compensation decisions are made based on factors including experience, skills, education, and other job-related factors, in accordance with our internal pay structure. We also offer a comprehensive benefits package, including health insurance, paid time off, and retirement plan.

 Full-time (Regular)

• 401(k)
• 401(k) matching 
• Dental insurance
• Health insurance
• Life insurance
• Paid time off
• Referral program
• Vision insurance
• Short/Long Term Disability