AI Prompt Senior Engineer-IN

About the Role

We are looking for a curious, analytically rigorous, and collaborative Data Scientist to join our growing team. In this role, you will work closely with business stakeholders, data engineers, and product teams to transform raw data into actionable insights and production‑ready machine learning solutions. You will own the full modeling lifecycle — from problem framing and data exploration through model development, validation, and deployment — with a primary focus on supervised learning applications including regression and classification.

This is an excellent opportunity for someone who has moved beyond foundational data science work and is ready to take on greater ownership of projects while continuing to grow their technical depth and business acumen.

Key Responsibilities and Duties

• Design, develop, and optimize prompts for Large Language Models (LLMs).
• Collaborate with data scientists and play a role in defining, testing and optimizing prompts that guide our AI systems to generate accurate, informative and creative outputs.
• Create and improve AI models and algorithms, as well as maintain prompt libraries to generate prompts for natural language processing (NLP) applications.
• Stay abreast of most recent developments on large language models.

Modeling & Machine Learning

• Develop, validate, and maintain supervised machine learning models including linear and logistic regression, decision trees, random forests, gradient boosting methods (XGBoost, LightGBM), and support vector machines.
• Apply sound practices around feature engineering, hyperparameter tuning, cross‑validation, and model selection to ensure robust, generalizable solutions.

Data Preparation & Exploration

• Partner with data engineering teams to source, clean, and transform structured and semi‑structured datasets.
• Conduct thorough exploratory data analysis to surface patterns, anomalies, and opportunities that inform both modeling strategy and business decisions.

Model Evaluation & Interpretation

• Apply appropriate evaluation metrics — such as RMSE, MAE, AUC‑ROC, precision‑recall, F1, R² and lift curves — to assess model performance in context.
• Leverage model explainability techniques (e.g., SHAP values, partial dependence plots) to communicate findings clearly to both technical and non‑technical audiences.

Deployment & Monitoring

• Collaborate with engineering and MLOps teams to package and deploy models into production environments.
• Establish monitoring frameworks to track model drift, data quality issues, and performance degradation over time, and lead remediation efforts when needed.

Stakeholder Engagement

• Translate complex analytical findings into clear, compelling narratives for business stakeholders.
• Contribute to project scoping discussions, help define success metrics, and proactively surface risks or limitations in proposed analytical approaches.

Mentorship & Knowledge Sharing

• Contribute to the team's collective growth by participating in code reviews, documenting work thoroughly, and sharing learnings through internal presentations or knowledge repositories.

Educational Requirements

• University (Degree) Preferred

Work Experience

• 3+ Years Required; 5+ Years Preferred

Physical Requirements

• Sedentary Work

Career Level

• 7IC

Required Qualifications

• 3 to 5 years of hands‑on experience in data science or a closely related quantitative role
• Strong proficiency in Python, including libraries such as scikit‑learn, pandas, NumPy, and matplotlib (experience in Domino Lab is a plus)
• Demonstrated experience building and deploying regression and classification models in a business context
• Solid understanding of statistical fundamentals including probability, hypothesis testing, and model assumptions
• Experience working with SQL for data extraction and transformation
• Familiarity with version control using Git and collaborative development practices
• Strong written and verbal communication skills with the ability to present technical work to diverse audiences

Preferred Qualifications

• Experience with cloud platforms such as AWS, Azure, or GCP and their respective ML services
• Familiarity with MLflow, Kubeflow, or similar experiment tracking and model registry tools
• Exposure to imbalanced classification problems and techniques such as SMOTE or cost‑sensitive learning
• Experience with time series regression or survival analysis
• Background in financial services, insurance, healthcare, or other regulated industries
• Bachelor's or advanced degree in Statistics, Mathematics, Computer Science, Data Science, or a related quantitative field

Related Skills

• Business Acumen
• Data Preprocessing
• Data Science
• Innovation
• Machine Learning (ML)
• Market/Industry Dynamics
• Predictive Modeling
• Programming
• Statistics