Data Scientist I

Join us in developing and applying cutting-edge machine learning solutions for commercial and industrial applications. As a Data Scientist I, you will partner with project teams to develop and deliver customer solutions, working on challenging problems in forecasting, demand planning, renewable energy optimization, anomaly detection, and prescriptive maintenance .

As a Data Scientist I , you will contribute to designing and delivering advanced AI solutions with a strong emphasis on Generative AI and Large Language Models (LLMs). You will apply applied science principles to help develop scalable, production-ready machine learning systems that drive measurable business impact. With a minimum 1–3 years of industry experience (or equivalent internship/research experience), you are expected to bring a solid foundation in statistical modeling, ML engineering, and modern AI architectures, particularly in GenAI and LLM-based applications. This role offers the opportunity to work on impactful AI initiatives while learning and growing within a fast-paced startup environment.

Support the design, development, and deployment of machine learning and Generative AI solutions to solve complex business problems.

Assist in building, fine-tuning, and optimizing Large Language Models (LLMs) and transformer-based architectures for real-world applications.

Apply applied science methodologies to experimentation, model evaluation, and performance optimization.

Contribute to developing scalable ML pipelines in collaboration with Engineering teams.

Support prompt engineering, model evaluation, benchmarking, and testing for GenAI applications.

Work closely with Product, Engineering, and Business stakeholders to translate requirements into data-driven AI solutions.

Contribute to model governance, responsible AI practices, and production monitoring efforts.

Stay current with advancements in Generative AI, LLM research, and applied machine learning.

Master’s degree (or Bachelor’s with strong experience) in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field.

With minimum 1–3 years of hands-on industry experience (including internships or research) in data science, machine learning, or applied AI roles.

Foundational experience with Generative AI frameworks and Large Language Models (e.g., exposure to transformer architectures, fine-tuning, or RAG concepts).

Proficiency in Python and familiarity with ML/AI libraries such as PyTorch, TensorFlow, Scikit-learn, or Hugging Face.

Solid understanding of statistical modeling and model evaluation techniques.

Exposure to deploying ML models or working in collaborative production environments.

Familiarity with cloud platforms (AWS, GCP, or Azure) is a plus.

Strong analytical thinking and willingness to work on evolving, sometimes ambiguous problem statements.

Good communication skills and ability to collaborate across teams.

Industry exposure in one or more of the following domains is desirable: Mining, Oil & Gas, Aerospace, Supply Chain, Logistics, or Renewable Energy.

As part of the interview process, you will be asked to complete a technical assessment.