Job Title: Expert Data Scientist/ Lead Data Scientist / Principal Data Scientist

About the Role

We are seeking a highly experienced Lead Data Scientist to spearhead advanced modeling initiatives and provide technical direction across cross-functional teams. This role is ideal for a strategic thinker with deep expertise in machine learning and the ability to bridge technical excellence with business value.

As a senior individual contributor, you will lead high-stakes data science projects end-to-end, shape team standards, and influence both product and business roadmaps. You’ll also work closely with engineering to ensure models are scalable and production-ready.

Key Responsibilities

• Design and implement advanced machine learning systems such as recommendation engines, anomaly detection systems, or time-series forecasting pipelines
• Define and enforce modeling best practices, review code, and set technical standards across the data science team
• Partner with product and engineering to influence roadmaps, product strategy, and customer-facing features
• Collaborate with ML engineers and data engineers on ML deployment, model versioning, and data architecture
• Lead experimental design and causal inference frameworks (e.g., uplift modeling, multivariate testing)
• Present technical findings to executive stakeholders and translate insights into strategic actions
• Mentor and coach other data scientists, contribute to hiring, and support technical upskilling across the org

Required Qualifications

Education:

• Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative field
• Master’s or PhD strongly preferred

Experience:

• 6–10 years of experience in data science or machine learning roles, including ownership of production-grade models
• Proven track record of solving complex business problems with ML at scale

Skills & Tools

• Deep knowledge of ML algorithms, statistical modeling, and AI frameworks
• Strong programming skills in Python (preferred) or R, and libraries like scikit-learn, XGBoost, TensorFlow, PyTorch
• Experience working with big data platforms (Spark, Hadoop, Dask) and cloud environments (AWS/GCP/Azure)
• Strong experience with MLOps, CI/CD, monitoring, and production pipeline design
• Excellent systems thinking and understanding of design patterns for ML infrastructure
• Strong communication, influence, and stakeholder management, including C-level engagement

Preferred Qualifications:

• Experience with real-time ML systems, reinforcement learning, or deep learning applications
• Publications, open-source contributions, or patents in data science or machine learning
• Prior involvement in hiring, technical interviews, or culture-building efforts within data teams