The company is looking to hire a junior-level Data Scientist to apply machine learning to real-world challenges in capital project analysis, turning data into impact for industries like energy, infrastructure, pharma, and consumer products.
Requirements
- 1–3 years of hands-on experience in data science or machine learning, through academic, research, or industry projects.
- Proficiency in Python and/or R, with working knowledge of SQL.
- Familiarity with machine learning libraries such as scikit-learn, PyTorch, or similar.
- Exposure to Large Language Models (LLMs) and experience implementing them in real-world workflows (e.g., RAG, agentic tools).
- Basic understanding of MLOps principles and version control tools like Git.
- Experience using cloud platforms (AWS, Azure) is a plus but not required.
- A project portfolio, GitHub profile, or sample work that showcases your technical skills and creativity.
Responsibilities
- Develop and deploy machine learning models to support predictive analytics and data-driven decision-making.
- Research and test new algorithms to improve model performance and efficiency.
- Build tools and applications using large language models (LLMs) for internal use and client engagement.
- Prepare, analyze, and present insights using structured and unstructured data.
- Collaborate with cross-functional teams and contribute to new product development.
- Work with stakeholders (technical and non-technical) to translate complex data into actionable recommendations.
- Contribute to the enhancement of benchmarking and analytics tools across multiple sectors.
Other
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
- Strong communication skills — able to explain technical ideas to non-technical stakeholders.
- Apply today with your resume, cover letter, portfolio (GitHub or similar), and salary expectations.
