Data Scientist (Webinar Test 1)

About the role

We are looking for a skilled and innovative Data Scientist to join our analytics team. This role involves leveraging data to drive business insights, build predictive models, and support strategic decision‑making. The ideal candidate is passionate about data, highly analytical, and experienced in machine learning and statistical modeling.

Responsibilities

• Serve as lead data strategist to identify and integrate new datasets that can be leveraged through our product capabilities, and work closely with the engineering team in the development of data products.
• Execute analytical experiments to help solve problems across various domains and industries.
• Identify relevant data sources and sets to mine for client business needs, and collect large structured and unstructured datasets and variables.
• Devise and utilize algorithms and models to mine big‑data stores; perform data and error analysis to improve models; clean and validate data for uniformity and accuracy.
• Analyze data for trends and patterns, and interpret data with clear objectives in mind.
• Implement analytical models in production by collaborating with software developers and machine‑learning engineers.

Required Qualifications

• Education: Bachelor’s or Master’s in a quantitative field such as Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related discipline.
• Experience: 2–5 years of hands‑on experience building and deploying machine‑learning solutions—especially recommender systems—in a SaaS or customer‑facing environment.
• Technical Proficiency: Proficient in Python (or R) and ML frameworks (scikit‑learn, TensorFlow, PyTorch); expertise with data tools (SQL, Spark, Airflow) and cloud platforms (AWS, GCP, Azure).
• AI & Next‑Gen Models: Demonstrated experience with embedding techniques, transformer‑based models, and graph ML for large‑scale recommendations.

Preferred Qualifications

• Experience working at a Fortune 500 company.
• LLM Proficiency: Hands‑on experience leveraging large language models (e.g., GPT‑4) for data augmentation, prompt engineering, or analytics automation.