Applied Analytics Engineer

Description

• Undertake meticulous preprocessing, cleansing, and transformation of large structured and unstructured datasets to ensure data quality, usability, and accuracy for modeling.
• Analyze large amounts of information to discover critical trends and patterns. Apply the scientific method to design experiments, formulate hypotheses, and conduct rigorous testing.
• Apply expertise in natural language processing (NLP) and text mining techniques where applicable.
• Handle cross-functional support duties, such as helping other departments with specific projects when required.
• Contribute to team efforts, including taking on new tasks as assigned by the supervisor.
• Apply understanding of data management and data engineering principles to maintain scalable data architecture.
• Assist with special projects as needed to support departmental goals.
• Design, build, and rigorously validate machine learning and statistical models (including regression, classification, clustering, and ensemble methods) for predictive and prescriptive analytics.
• Design, build, and maintain robust and scalable data pipelines to process, transform, and organize large, complex datasets from disparate sources. Identify, assess, and integrate valuable data sources, developing automated processes for continuous data collection and ingestion.
• Design dimensional data models using methodologies to ensure enterprise data consistency.
• Design and implement data-grounded AI agents using large language models (LLMs) and specialized toolkits (e.g., LangChain, agent frameworks) to automate complex decision-making and data querying workflows.

Requirements

• Basic idea or hands on with Tableau or similar data visualization tools/stacks.
• Experience leading or significantly contributing to the development of complex data solutions.
• Hands-on experience with MLOps, Git Version Control, Unit/Integration/End-to-End Testing, CI/CD, and release management processes.
• Practical experience with big data processing frameworks such as Spark or similar distributed computing environments.
• 5 years of demonstrable experience in a data-focused role encompassing data exploration, data cleaning, and data visualization. Experience with cloud platforms (AWS, Azure, GCP)
• 5 years of experience with statistical and programming languages for data analysis, specifically Python (including PySpark, NumPy, Pandas, Scikit-learn) and SQL.
• Familiarity with project management principles and best practices.
• Hands-on experience with Snowflake, JIRA or ServiceNow.
• Extensive experience developing predictive data models, quantitative analyses, and visualization of large data sources, including both structured and unstructured data.
• Use deep analytical skills and data science knowledge to address complex, real-world business challenges and drive measurable impact.
• Hands-on expertise in data management, programming, and processing large data volumes using technologies such as Python, SQL, and PySpark.

Benefits

Training + Development

Interview process

Visa Sponsorship

Security clearance