Design, build, and maintain scalable batch and real-time data pipelines powering RFID analytics
Develop, deploy, and monitor end-to-end machine learning pipelines, including feature engineering, training, and evaluation
Collaborate closely with research teams to improve RFID-based positioning algorithms and core modeling approaches
Design and execute experiments and simulations from hypothesis generation through analysis and presentation of results
Perform deep analytical investigations into datasets and systems to identify root causes and drive data-informed improvements
Establish and evolve best practices for data science, experimentation, and research workflows
Uphold a high standard of scientific rigor, statistical validity, and reproducibility across all work
Partner with product managers, designers, and engineering teams to translate product requirements into data-driven solutions and features
Build and maintain data products and dashboards that support real-time and near real-time insights
Required:
You have a Bachelor’s degree with equivalent practical experience or Master’s degree in a relevant field (e.g., Data Science, Computer Science, Statistics, Engineering)
You have 2+ years of experience in a data science or applied machine learning role
You have strong proficiency in Python and common data science libraries (NumPy, Pandas, SciPy, PyTorch etc.)
You have strong SQL skills for data exploration, transformation and Machine Learning feature development
You have knowledge of streaming technologies for real-time analytics and Machine Learning feature engineering
You have solid foundation in statistics, probability, and linear algebra
You have experience with both classical machine learning and modern deep learning techniques
You have experience contributing to the end-to-end development lifecycle, from requirements gathering and feature development to model / pipeline implementation and deployment
You have excellent communication and collaboration skills, with the ability to work cross-functionally
Preferred:
You have experience with GCP data and ML stack, including BigQuery, Dataflow, Vertex AI
You have experience orchestrating pipelines using Airflow and/or Kubeflow Pipelines
You have experience with streaming data architectures and real-time data processing frameworks (e.g., Pub/Sub, Kafka)
You have familiarity with stream processing frameworks such as Apache Beam or Spark Structured Streaming
You have experience building real-time or near real-time analytics systems
You have familiarity with BI and visualization tools such as Looker
You have experience working with large-scale data systems and complex feature engineering pipelines
WHAT YOU'LL DO
In your first 30 days, you will:
Onboard to our data and ML ecosystem, including Airflow, Dataflow, BigQuery, Vertex AI, and Looker
Gain familiarity with our RFID analytics platform, data models, and existing pipelines
Partner with team members to understand current projects, workflows, and coding standards
Start contributing to small tasks such as debugging pipelines, improving data quality checks, or enhancing existing features
In your first 60 days, you will:
Take ownership of well-scoped features or pipeline components, from requirements to implementation with guidance
Contribute to feature engineering and model improvements within existing ML pipelines
Build or enhance batch and/or real-time data pipelines
Develop or enhance dashboards or data products in Looker
Participate in experiment design and analysis to support improvements in system performance metrics
In your first 90 days, you will:
Independently deliver end-to-end features, from requirements gathering through deployment into production
Collaborate with Data Engineering in the design and implementation of scalable streaming or batch architectures
Identify and drive improvements in data quality, pipeline reliability, or model performance
Collaborate cross-functionally with product, design, and research to shape new data-driven features
Begin contributing to best practices in experimentation, data science workflows, and pipeline development
*At RADAR, your base pay is one part of your total compensation package. The expected base salary range for this position is $95,000 - $145,000. Individual pay is determined by work location and additional factors, including job-related skills, experience and relevant education or training.*You will also be eligible to receive other benefits including: equity, comprehensive medical and dental coverage, life and disability benefits, 401k plan, flexible time off, and paid parental leave. The pay range listed for this position is a good faith and reasonable estimate of the range of possible base compensation at the time of posting.
Research has shown that women & underrepresented minorities are more likely to read lists of requirements and consider themselves unqualified if they don't meet every single one. This list represents what we're ideally looking for, but everyone has unique strengths & weaknesses, and we hire for strength & potential, not lack of weakness.
Use of artificial intelligence or a LLM such as ChatGPT during the interview process will be grounds for rejection of your application process.
