Fulltime - Sr. Data Scientist

Role: Sr. Data Scientist

Location: Remote, however travel might be required as per business requirements

Where you’re headed

You’ll creatively use data to solve business challenges, often uncovering new and transformative opportunities along the way. Applying advanced analytics, quantitative tools, and modeling techniques, you’ll interpret and offer recommendations based on insights from the data.

Where you’ve been

You’re a problem solver, data expert, analyst, and communicator, who can create new algorithms from scratch. You have an advanced degree in a quantitative field, such as computer science, engineering, physics, statistics or applied mathematics, and have:

Job Description:

• Familiarity with statistical techniques, data mining, hypothesis testing, and exploratory data analysis

• Strong knowledge of programming languages with a focus on machine learning and advanced analytics (such as Python, R, Scala)

• Experience working with large datasets, data pipelines, and relational databases

• A collaborative mindset with the ability to communicate complex analytical concepts effectively to both technical and non technical stakeholders

• Excellent problem solving skills with the ability to analyze issues, identify root causes, and recommend solutions quickly

• Proven experience building, validating, and deploying machine learning models end to end in production environments

• Strong understanding of model performance evaluation, experiment design, and model lifecycle management

• Ability to translate business problems into analytical frameworks and measurable success metrics

• Experience working cross functionally with engineering, product, and business teams to deliver impact at scale

Good to Have:

• Experience with Generative AI, including large language models (LLMs), prompt engineering, fine tuning, and retrieval augmented generation (RAG)

• Exposure to Agentic AI concepts, such as autonomous agents, tool usage, planning, memory, and multi agent workflows

• Familiarity with modern ML/AI frameworks (e.g., PyTorch, TensorFlow) and MLOps tools for deployment and monitoring

• Experience deploying AI solutions on cloud platforms (Azure, AWS, or GCP) with an understanding of scalability and cost considerations

• Knowledge of responsible AI practices, including model explainability, bias mitigation, and governance