Data & ML
Leverage data and machine learning to drive insights and build intelligent features.
Choose a Specific Role
Select one of the roles below to start your tailored interview.
Junior Data Analyst
Analyze data to uncover trends and provide insights to the business.
Seattle, WA
1+ year
Full-Time
Responsibilities
- Gather and clean data from various sources.
- Perform exploratory data analysis to identify trends and patterns.
- Create dashboards and visualizations to communicate findings.
- Support various teams with ad-hoc data requests.
Qualifications
- Proficiency in SQL and a data analysis language like Python (with Pandas) or R.
- Experience with data visualization tools like Tableau or Power BI.
- Strong analytical and problem-solving skills.
- Attention to detail and a commitment to data accuracy.
Machine Learning Engineer
Build and productionize machine learning systems at scale.
Remote
3+ years
Full-Time
Responsibilities
- Design and implement scalable pipelines for data processing and model training.
- Deploy machine learning models into production environments.
- Monitor and maintain the performance of production models.
- Collaborate with data scientists to bring their models to production.
Qualifications
- Strong software engineering skills, particularly in Python or a similar language.
- Experience with MLOps tools and platforms (e.g., Kubeflow, MLflow).
- Knowledge of cloud infrastructure and containerization (AWS, Docker, Kubernetes).
- Familiarity with machine learning concepts and frameworks.
Senior Data Scientist
Build machine learning models to solve key business problems.
New York, NY
5+ years
Full-Time
Responsibilities
- Design, build, and deploy machine learning models.
- Conduct in-depth statistical analysis to understand user behavior.
- Communicate complex findings to both technical and non-technical audiences.
- Mentor junior data scientists and contribute to our data science practice.
Qualifications
- A Ph.D. or M.S. in a quantitative field like Computer Science, Statistics, or a related discipline.
- Extensive experience with Python and machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch).
- Deep understanding of machine learning theory and statistical methods.
- Experience with large-scale data processing tools like Spark.