Role overview
Employment type: Full-time
Pay: $130,000 - $135,000 per year
Work arrangement: Remote, with travel as required by business needs
Data science role overview
You’ll use advanced analytics, statistical methods, and machine learning to solve complex business problems and uncover opportunities hidden in large datasets. The role combines hands-on modeling with business problem solving, requiring you to turn data into practical recommendations and production-ready solutions.
Analytics and machine learning
- Apply statistical analysis, data mining, hypothesis testing, and exploratory data analysis to business questions.
- Design new analytical approaches and algorithms when existing methods are not sufficient.
- Build, validate, and deploy machine learning models through the full production lifecycle.
- Define appropriate model evaluation methods, experiments, and measurable success criteria.
- Monitor model performance and support ongoing model lifecycle management.
- Translate business challenges into structured analytical problems and actionable recommendations.
Data and technical work
- Use Python, R, Scala, or similar languages for machine learning and advanced analytics.
- Work with large datasets, relational databases, and data pipelines.
- Analyze technical and business issues, identify root causes, and recommend effective solutions.
- Partner with engineering, product, and business teams to deliver data-driven solutions at scale.
- Communicate analytical findings clearly to both technical and non-technical stakeholders.
Core qualifications
- Advanced degree in computer science, engineering, physics, statistics, applied mathematics, or another quantitative field.
- Strong knowledge of statistical methods and machine learning techniques.
- Hands-on experience developing machine learning solutions for production environments.
- Experience working with large-scale data and relational database systems.
- Strong programming skills in Python, R, Scala, or comparable analytical languages.
- Understanding of experiment design, model evaluation, and performance measurement.
- Ability to convert business objectives into analytical frameworks and measurable outcomes.
- Strong problem-solving and root-cause analysis skills.
- Ability to collaborate effectively across technical and business functions.
- Clear written and verbal communication skills.
AI experience that adds value
- Experience with generative AI, large language models, prompt engineering, fine-tuning, or retrieval-augmented generation.
- Exposure to agentic AI concepts, including autonomous agents, tool use, planning, memory, and multi-agent workflows.
- Familiarity with machine learning frameworks such as PyTorch or TensorFlow.
- Experience with MLOps tools used for model deployment, monitoring, and lifecycle management.
- Experience deploying AI or machine learning solutions on Azure, AWS, or GCP.
- Understanding of scalability, cloud costs, and production deployment considerations.
- Knowledge of responsible AI practices, including explainability, bias mitigation, and governance.