CapTech is looking for Machine Learning Engineers to design and implement data-driven solutions for clients, focusing on building and deploying scalable machine learning systems in enterprise environments.
Requirements
- Hands-on experience manipulating and analyzing large (multi-billion record) data sets.
- Hands-on experience developing data-driven solutions using Python, Scala, or similar languages.
- Proficiency leveraging SQL, Spark, NoSQL, and/or cloud data processing frameworks in a production setting.
- Proficiency with containerization (e.g., Docker) and microservices.
- Proficiency with data warehousing tools/environments such as Snowflake, Databricks, Azure SQL, Amazon RDS
- Hands-on experience implementing production-scale machine learning systems in one or more domains (i.e., personalization, natural language processing, computer vision).
- Knowledge of DevOps and automation best practices.
Responsibilities
- Strategizing with clients, data scientists, engineers, and other members of cross-functional teams to implement end-to-end machine learning solutions and identify new machine learning and data science approaches to meet business needs
- Deconstructing client needs into data-driven processes/models and analytical measures.
- Analyzing and transforming large datasets hosted on a variety of enterprise-level data platforms (e.g., AWS, Azure, GCP).
- Designing, developing, and deploying advanced analytical solutions leveraging client data (e.g., recommender systems, natural language processing, risk scoring).
- Productionizing ML systems with a focus on optimization and scalability to satisfy clients’ requirements.
- Growing CapTech’s Machine Learning and Data Science practices through delivering client presentations, writing proposals, attending various business development events, and leading teams of junior data scientists and engineers.
Other
- Bachelor's degree or equivalent combination of education and experience.
- Comfort and proficiency in framing data-driven problems from cross-industry business requirements.
- Experience applying analytical methods across multiple business domains (e.g., customer analytics, marketing, finance, digital channels)
- Knowledge of statistics and statistical modeling methods.
- Knowledge of model management and model versioning best practices.
