Negotiable
Outside
Remote
USA
Summary: The Sr. Data Science / Sr. Machine Learning Engineer role is a client-facing position requiring strong communication skills and expertise in machine learning and data science. The successful candidate will be responsible for building and enhancing customer data science workloads, developing LLM solutions, and providing mentorship to data teams. This role emphasizes collaboration and the application of best practices in MLOps across various domains. The position is remote and offers a contract duration of 6-12+ months.
Key Responsibilities:
- Build and increase customer data science + machine learning workloads and apply the best MLOps to productionize these workloads across a variety of domains
- Develop LLM solutions on customer data such as RAG architectures on enterprise knowledge repos, querying structured data with natural language, and content generation
- Advise data teams on several data science areas such as architecture, tooling, and best practices
- Provide technical mentorship to the larger ML Subject Matter Expert community
Key Skills:
- 5+ years of hands-on industry data science experience, using typical machine learning and data science tools including pandas, mlflow, scikit-learn, gensim, nltk, and TensorFlow/PyTorch
- Experience with the latest techniques in natural language processing including vector databases, fine-tuning LLMs, and deploying LLMs with tools such as HuggingFace, Langchain, and OpenAI
- Experience building production-grade machine learning deployments on AWS, Azure, or Google Cloud Platform including drift monitoring
- Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research) or equivalent practical experience
- Experience communicating and teaching technical concepts to non-technical and technical audiences alike
- Passion for collaboration, life-long learning, and driving value through ML
- [Required] Experience working with Apache SparkTM to process large-scale distributed datasets
- [Required] Experience working with the Databricks platform, mlFlow and popular data science libraries.
- [Required] CI/CD experience with Git, Azure pipelines, DevOps
- [Required] 2+ years customer-facing experience
- Can meet expectations for technical training and role-specific outcomes within 1 month of hire
- Preferred Certifications:
- Databricks Associate ML Engineer
- Databricks Professional ML Engineer
- Databricks Associate GenAI Engineer
- Databricks Data Engineer
Salary (Rate): undetermined
City: undetermined
Country: USA
Working Arrangements: remote
IR35 Status: outside IR35
Seniority Level: undetermined
Industry: IT
*NOTE: This is client facing role, need an excellent communicator and strong with ML/ Data Science.
*NOTE: This is client facing role, need an excellent communicator and strong with ML/ Data Science.
*NOTE: This is client facing role, need an excellent communicator and strong with ML/ Data Science.
Key Skills
- Build and increase customer data science + machine learning workloads and apply the best MLOps to productionize these workloads across a variety of domains
- Develop LLM solutions on customer data such as RAG architectures on enterprise knowledge repos, querying structured data with natural language, and content generation
- Advise data teams on several data science areas such as architecture, tooling, and best practices
- Provide technical mentorship to the larger ML Subject Matter Expert community
- 5+ years of hands-on industry data science experience, using typical machine learning and data science tools including pandas, mlflow, scikit-learn, gensim, nltk, and TensorFlow/PyTorch
- Experience with the latest techniques in natural language processing including vector databases, fine-tuning LLMs, and deploying LLMs with tools such as HuggingFace, Langchain, and OpenAI
- Experience building production-grade machine learning deployments on AWS, Azure, or Google Cloud Platform including drift monitoring
- Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research) or equivalent practical experience
- Experience communicating and teaching technical concepts to non-technical and technical audiences alike
- Passion for collaboration, life-long learning, and driving value through ML
- [Required] Experience working with Apache SparkTM to process large-scale distributeddatasets
- [Required] Experience working with the Databricks platform, mlFlow and popular datascience libraries.
- [Required] CI/CD experience with Git, Azure pipelines, DevOps
- [Required] 2+ years customer-facing experience
- Can meet expectations for technical training and role-specific outcomes within 1 monthof hire
- Preferred Certifications:
- Databricks Associate ML Engineer
- Databricks Professional ML Engineer
- Databricks Associate GenAI Engineer
- Databricks Data Engineer