Lead Data Scientist

Job Description

Lead Data Scientist | AI-driven Tech Company

The Tone:
This is a full-time role at AI-driven Tech Company, located at an undisclosed office location. The company focuses on transforming industries through intelligent data solutions, applying machine learning and statistical methods to solve real-world problems. This role is important for driving projects and delivering insights that influence business decisions, contributing to a culture of continuous learning and impact.

The TL;DR
• Role: Full Time
• Location: Undisclosed

• Team: Leads and mentors a team of data scientists; reports to an unspecified manager.
• Mission: Responsible for the end-to-end development and deployment of advanced analytical models, translating complex data into actionable insights that inform strategic business decisions.
• Tech Stack: Python, NumPy, Pandas, Scikit-learn, TensorFlow/PyTorch, SQL, AWS, Azure, GCP, Spark, Dask, MLflow, Kubeflow, Airflow

What You’ll Actually Do
• Project Leadership: Lead and execute complex data science projects from conception to deployment, including problem definition, model development, validation, and monitoring.
• Model Development: Design and implement robust machine learning models (e.g., predictive, prescriptive, generative AI) for various applications, optimizing for performance, scalability, and interpretability.
• Team Mentorship: Mentor and guide junior and mid-level data scientists, fostering technical excellence, best practices, and continuous learning within the team.
• Cross-functional Collaboration: Collaborate with product managers, engineers, and business analysts to identify opportunities, define requirements, and integrate data science solutions into production systems.
• Infrastructure Development: Develop and maintain data pipelines, feature stores, and model deployment infrastructure in collaboration with MLOps and Data Engineering teams.

The Must-Haves
• Background: Master’s or Ph.D. in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field. This role requires a strong foundation in statistical modeling, machine learning algorithms, experimental design, and causal inference.
• Experience: 7+ years of experience in data science or machine learning roles, with at least 2 years in a leadership or senior capacity. Candidates should have a proven ability to develop, validate, and deploy production-grade machine learning models, along with experience in A/B testing, hypothesis testing, and statistical significance.
• Skills: Expert proficiency in Python and its data science ecosystem (NumPy, Pandas, Scikit-learn, TensorFlow/PyTorch). Strong SQL skills and extensive experience working with large-scale datasets in cloud environments (AWS, Azure, GCP). Excellent communication skills to articulate complex technical concepts to diverse audiences.
• Bonus: Experience with distributed computing frameworks (e.g., Spark, Dask); familiarity with MLOps tools and practices (e.g., MLflow, Kubeflow, Airflow); prior experience in a specific industry (e.g., Fintech, Healthcare, E-commerce); contributions to open-source projects or a strong portfolio of personal projects; experience working with generative AI models or large language models (LLMs).