Junior Data Scientist

About the role

Emerson is looking for a Junior Data Scientist to help shape smarter, faster commercial decisions through advanced analytics, machine learning, and responsible AI. You will work closely with data science, engineering, pricing, and business teams to turn complex data into clear, explainable recommendations that improve pricing strategy and customer outcomes.

Key responsibilities

• Support development and implementation of statistical, machine learning, and AI models for product development, pricing, and customer experience.
• Design, refine, and improve rule‑based and analytical decision‑support logic used in commercial processes.
• Stay current with research and prototype new data‑science and AI techniques, including responsible use of generative AI.
• Collaborate with cross‑functional teams to identify analytics opportunities that deliver measurable value.
• Translate commercial policies and constraints into analytically sound, explainable solutions.
• Analyze historical and transactional data to calibrate recommendations, validate outcomes, and assess business impact.
• Communicate findings and trade‑offs through documentation, presentations, and visualizations for both technical and non‑technical audiences.
• Follow data‑privacy, security, and responsible AI practices, raising risks and assumptions proactively.

Required profile

• 1–3 years of experience (including internships, co‑ops, or academic projects) applying statistics and Python to real‑world datasets.
• Bachelor’s degree in Statistics, Mathematics, Computer Science, Data Science, Engineering, or a related field (master’s a plus).
• Fluent in English, both written and verbal.
• Strong problem‑solving mindset, curiosity, and ability to communicate technical findings to diverse audiences.

Required skills

• Python for data analysis (pandas, NumPy).
• Applied statistics and experimentation (hypothesis testing, confidence intervals, A/B testing).
• Core machine‑learning concepts (classification, regression, model evaluation).