2+ years’ experience in data science, advanced analytics, or a related field
Bachelor’s degree in a technical field such as Computer Science, Statistics, Mathematics, Data Science, Engineering, Machine Learning, or a closely related discipline
Excellent communication skills (written & spoken), with the ability to explain complex concepts to non-technical audiences
Strong problem-solving abilities: approach challenges analytically and creatively, using data-driven methods to identify root causes and develop effective solutions; comfortable tackling ambiguous problems and breaking them down into actionable steps
Python expertise: highly proficient in Python for data analysis, modeling, and automation, with the ability to write clean, efficient, and well-documented code
SQL proficiency: strong skills in SQL for querying, manipulating, and managing large datasets across different database systems
Good coding skills: comfortable with general programming concepts, version control (e.g., Git), and collaborative codebases
Experience developing and tracking Sales, Marketing, Finance, and Commercial metrics
Expertise in Financial Modeling, Sensitivity Analysis, and A/B Testing
Hands-on experience with cloud platforms (Snowflake, AWS, GCP, Azure) and modern data architectures
Proficiency with Data Mining and ETL tools (SQL, Python, R, db2, Alteryx, Knime, etc.)
Experience designing and implementing real-time data pipelines and handling streaming data
Proficiency with data visualization tools such as Power BI, Tableau, or similar platforms to communicate insights effectively
Familiarity with Generative AI, Large Language Models (LLMs), and their business applications (comfortable using and learning about these technologies)
Experience with Forecasting, Risk Modeling, Product Portfolio Optimization, Market Mix Modeling, LTV Modeling, and Financial Forecasting
Nice to Have: Experience in Manufacturing, CPG, or Retail sales environments
Nice to Have: Software development skills, including source code management (GitHub, BitBucket, Azure DevOps Repos, etc.)
Nice to Have: Experience with MLOps, model deployment, and monitoring in production env
Responsibilities
Deliver actionable insights by collecting, mining, and analyzing complex data sets to drive business decisions and growth
Apply data science, machine learning, and artificial intelligence—including emerging Generative AI tools and techniques—to solve real-world business challenges
Stay current with the latest technologies (Gen AI, LLMs, cloud-native analytics) and leverage them to enhance analytics capabilities
Collaborate with stakeholders to define goals, select KPIs, monitor performance, and identify trends and opportunities
Identify, design, and implement data process improvements, such as automating manual workflows, optimizing data infrastructure for scalability, and enhancing data delivery
Diagnose system deficiencies and recommend practical, effective solutions
Partner with IT and business teams to shape analytics and technology roadmaps aligned with strategic objectives
Champion a data-driven culture, working with analytics and commercial experts to maximize the value of data assets
Provide robust data foundations for insights across Operations/Supply Chain, Marketing, Finance, Commercial, and more