- Builds advanced models using statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, etc.
- Designs the data pipelines and engineering infrastructure to support clients' enterprise machine learning systems at scale
- Takes offline models built by data scientists and turn them into a real machine learning production system
- Develops and deploys scalable tools and services for our clients to handle machine learning training, inference, and monitoring
- Identifies and evaluates new technologies to improve performance, maintainability, and reliability of our clients' machine learning systems
- Works closely with data scientists and data engineers to explore new data sources, design ML features, and build capabilities for feature management (e.g., feature store)
- Applies software engineering rigor and best practices to machine learning, including CI/CD, automation, etc.
- Supports model lifecycle development, with an emphasis on auditability, versioning, and data security
- Facilitates the development and deployment of proof-of-concept machine learning systems
- Communicates with clients to build requirements and track progress
- Supervises the day-to-day workload of Senior Associates and Associates on assigned engagements and reviews work product within a single or multi-disciplined valuation assignments
- Undergraduate degree in Computer Science, Data Science, Information Sciences, Econometrics, required
- Master's degree, preferred
- Five (5) or more years of experience building and deploying machine learning (ML) solutions into production, required
- Three (3) or more years of experience working with one of the cloud computing environments such as AWS, Azure, Google Cloud, required
- Experience in operationalization of End-to-End Data Science projects (MLOps), required
- Solid understanding of ML concepts, Model Lifecycle including data connection, ETLs, model training and deployment and the tools necessary to test and monitor models in productions and related best practices, required
- Experience with Machine Learning enablement tools such as experiment tracking systems, feature store and similar system, preferred
- Experience developing with containers and Kubernetes in cloud computing environments, preferred
- Experience translating business needs to technical requirements, preferred
- Experience in consulting industry serving financial services clients, preferred
- Experience in risk analytics (model development, strategy and framework, scorecard development, documentation, validation, governance, implementation, and automation etc.) will be a strong advantage, preferred
- N/A
- Experience in relational database technologies like Snowflake, BigQuery, Redshift, Oracle, SQL Server or object storage services like S3, required
- Experience with one or more data-oriented workflow orchestration frameworks (KubeFlow, Airflow, MLflow, Argo, etc.), required
- Strong software engineering skills in complex, multi-language systems including fluency in Python, required
- N/A
- Advanced knowledge of ML frameworks such as TensorFlow, PyTorch, etc., preferred
- Ability to deploy production-grade ML models using at least one of the popular frameworks or platforms (for example, Kubeflow, AzureML, AWS Sagemaker, Databricks etc.), preferred
- A self-starter and problem solver owning complex and impactful projects in a fast-paced environment
- Passion for continuous learning, self-development and building impactful products
Manager, Valuation - Houston, United States - BDO
Description
Job Summary:
The Manager, Valuation & Capital Market Analysis – Complex Financial Instruments – MLOps will be a member of the Data Science/MLOps team that builds Model and related infrastructure to speed up the development and deployment of ML and Statistical models at scale and bring game-changing impact to our client's decisioning. You will collaborate with data scientists, data engineers, and software engineers to deliver ML applications in client's tech environment. You should be comfortable working independently and as part of a team.
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Individual salaries that are offered to a candidate are determined after consideration of numerous factors including but not limited to the candidate's qualifications, experience, skills, and geography.
California Range: $120,000 - $160,000
Colorado Range: $120,000 - $160,000
NYC/Long Island/Westchester Range: $120,000 - $160,000
Washington Range: $120,000 - $160,000