Keystone AI is looking to solve complex challenges in competition, strategy, and intellectual property for leading technology firms and global brands by leveraging data, platform, and forensic expertise. The K.ATS Foundry aims to build secure, reusable infrastructure and scalable technical solutions to accelerate project delivery and ensure data-driven outcomes.
Requirements
- Experience working on data analysis, data science, ETL/ELT on large datasets
- Progressive experience architecting cloud-based solutions on one of the major cloud provider platforms (AWS/GCP/Azure)
- Advanced proficiency working with data analysis languages (Python, R, SQL)
- Advanced proficiency working with machine learning libraries and tools (Pytorch, Keras, TensorFlow, Transformers, NLTK, scikit-learn, OpenAI API, etc.)
- Experience with data orchestration tools (Airflow, dbt, Prefect, Luigi, etc.)
- Big data platform experience (Snowflake, Spark, BigQuery, etc.)
- Experience with CI/CD pipelines is required
Responsibilities
- Architecting and driving the design and implementation of our data infrastructure and systems, and addressing various data engineering, data science, software development needs of our client-facing teams with data-driven approaches.
- Consult with client-facing teams to design and architect data infrastructure to automate manual processes, optimize data delivery and processing, and ensure user experience.
- Responsible for managing project big data requests and challenges by building reproducible data ingestion pipelines and downstream analytic processes to derive value for the teams.
- Develop custom software solutions such as APIs to interact with large language models or end-user portals to search through high volumes of data easily.
- Leverage the appropriate infrastructure required for optimal extraction, transformation, and loading (ETL/ELT) of data from a wide variety of data sources using SQL and ‘big data’ technologies.
- Lead creation, maintenance, and implementation of in-house tools, libraries, and systems to increase the efficiency and scalability of the team.
- Build tools and APIs to deploy data science and machine learning systems at scale on projects.
Other
- Evaluate client needs, propose suitable recommendations, and carry out tailored implementation for various practices and projects.
- Lead the adoption of best practices in data engineering and software development.
- Work closely with cross-functional teams of data scientists, engagement managers, and consultants to identify areas of opportunity and value.
- Experience in leading data teams preferred
- Bachelor's degree
