What are the responsibilities and job description for the Associate Director of Data Science and MLOPs position at Jobs via Dice?
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We are looking for a senior Data Science and MLOps leader with deep hands-on experience building, deploying, and operationalizing machine learning solutions.
This person should be able to operate at both a strategic and hands-on level — partnering with business, data science, engineering, and cloud teams to define ML use cases, design scalable solutions, and ensure models are production-ready, monitored, governed, and maintainable.
The ideal candidate has experience leading ML initiatives from concept through production, building MLOps frameworks, improving model deployment pipelines, and helping organizations mature their machine learning capabilities, and establishing best practices. This is an opportunity to build a robust Data Science/ML Ops practice and team from the ground up according to your vision and based on what is best for end clients.
Key Responsibilities
We are looking for someone who can bring senior-level ownership to Data Science and MLOps initiatives. This person should understand how to build models, but more importantly, how to make models work reliably in production.
The right candidate will be comfortable getting hands-on while also guiding teams, setting standards, improving processes, and helping stakeholders understand what is needed to scale machine learning successfully.
This is not a pure research or notebook-only data science role. We need someone who understands the full lifecycle of ML delivery — from data and experimentation through deployment, monitoring, governance, and ongoing optimization.
We are looking for a senior Data Science and MLOps leader with deep hands-on experience building, deploying, and operationalizing machine learning solutions.
This person should be able to operate at both a strategic and hands-on level — partnering with business, data science, engineering, and cloud teams to define ML use cases, design scalable solutions, and ensure models are production-ready, monitored, governed, and maintainable.
The ideal candidate has experience leading ML initiatives from concept through production, building MLOps frameworks, improving model deployment pipelines, and helping organizations mature their machine learning capabilities, and establishing best practices. This is an opportunity to build a robust Data Science/ML Ops practice and team from the ground up according to your vision and based on what is best for end clients.
Key Responsibilities
- Lead the design, development, deployment, and operationalization of machine learning models and AI solutions,
- Build and maintain scalable MLOps workflows and modern CI/CD practices.
- Partner with data engineers, cloud architects, and business stakeholders to translate business problems into ML solutions.
- Design model training, validation, deployment, monitoring, and retraining pipelines.
- Establish best practices for model versioning, experiment tracking, feature engineering, model governance, and production monitoring.
- Support deployment of ML models into production environments using endpoints (AWS SageMaker, Google Cloud Platform Vertex AI, or other solutions), batch transform, pipelines, model registry.
- Lead technical discovery and solution design for new ML/AI use cases.
- Help define MLOps standards, frameworks, documentation, and repeatable delivery patterns.
- Collaborate with DevOps/CloudOps teams on infrastructure, security, access controls, cost optimization, and deployment automation.
- Mentor data science and engineering teams on production ML best practices.
- Communicate technical recommendations, risks, tradeoffs, and outcomes to senior stakeholders.
- 7 years of experience across data science, machine learning engineering, MLOps, data engineering, or related technology roles. Experience at a Lead, Principal, Manager, Associate Director, Director, or similar senior level.
- 3 years of experience deploying and managing machine learning solutions in production environments, ideally some recent experience in supply chain.
- Experience designing and building MLOps pipelines for model training, deployment, monitoring, and retraining.
- Strong Python experience for data science, ML development, automation, and model operations.
- Experience with common ML frameworks and libraries such as scikit-learn, TensorFlow, PyTorch, XGBoost, Hugging Face, or similar.
- Experience with AWS services such as S3, Lambda, IAM, Glue, Athena, Redshift, ECR, ECS/EKS, CloudWatch, Step Functions, EventBridge, or similar.
- Experience with CI/CD tools and practices, including GitHub Actions, GitLab CI, Jenkins, CodePipeline, or similar.
- Strong understanding of model governance, model monitoring, model drift, versioning, reproducibility, and production reliability.
- Ability to lead cross-functional teams and communicate effectively with technical and business stakeholders.
- Experience building MLOps capability from the ground up or improving immature ML delivery processes.
- Experience with SageMaker Pipelines, SageMaker Model Registry, SageMaker Feature Store, SageMaker Studio, and SageMaker endpoints.
- Experience with infrastructure as code tools such as Terraform, CloudFormation, or CDK.
- Experience with containerization and orchestration using Docker, Kubernetes, ECS, or EKS.
- Experience with data platforms, lakehouse architecture, feature stores, data pipelines, and cloud-native analytics.
- Experience with generative AI, LLMOps, RAG pipelines, embeddings, vector databases, or model evaluation frameworks.
- Experience in consulting, agency, enterprise, or client-facing environments.
- Experience developing technical roadmaps, operating models, governance frameworks, and executive-facing recommendations.
We are looking for someone who can bring senior-level ownership to Data Science and MLOps initiatives. This person should understand how to build models, but more importantly, how to make models work reliably in production.
The right candidate will be comfortable getting hands-on while also guiding teams, setting standards, improving processes, and helping stakeholders understand what is needed to scale machine learning successfully.
This is not a pure research or notebook-only data science role. We need someone who understands the full lifecycle of ML delivery — from data and experimentation through deployment, monitoring, governance, and ongoing optimization.