Lead Data Scientist

Overview

Job Responsibilities: Require 6+ years of experience

• Define, plan and execute analyses regarding innovation initiatives, methodology development and improvement as well as process automation.
• Prototype solutions and support pilot programs for R&D purposes, including trend analyses, representation/sampling, bias reduction, indirect estimation, data integration, automation, and generalization.
• Test-driven development of scalable data processing applications.
• Deliver high quality documentation of new methodologies and best practices.
• Collaborate with experienced Developers, Data Scientists, and Technology engineers.
• Support various Operations team as main users of our solutions.
• Engage with stakeholders on scope, execution, data exchange, and outcomes for assigned projects.
• Participate in multiple projects simultaneously.

Machine Learning & Artificial Intelligence

• Machine Learning: Knowledge of various algorithms and their appropriate application for prediction and classification.
• Deep Learning: Understanding of neural networks and deep learning frameworks like TensorFlow and PyTorch.
• GenAI and AgenticAI: Knowledge of available applications and programming packages for implementation of LLM based solutions and data processing automation.

Data Visualization & Presentation

• Data Visualization: The ability to create clear and compelling charts and graphs using tools like Tableau, Power BI, or Matplotlib to communicate findings to stakeholders.

Databases & Cloud

• Database Management: Proficiency with both SQL and NoSQL databases (like MongoDB) for data storage and retrieval.
• Cloud Computing: Experience with cloud platforms such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud

Requirements:

• Extensive experience in trend analyses, multivariate statistics (parametric/non-parametric), sampling, bias reduction, indirect estimation, data aggregation techniques, automation, and generalization.
• High proficiency in Python programming language including data analysis and statistical packages (Pandas, NumPy, Scikit-Learn). Good familiarity with Python standard library, especially unittest and argparse modules.
• Experience with Spark or other big data processing solutions.
• Experience in machine learning and GenAI / AgenticAI applications
• Experience with cloud computing and storage (MS Azure preferred).
• Experience with Docker and Linux command line.
• Ability to quickly manipulate, analyze, and interpret large data sources.
• Strong communication/writing skills with good English (working in a remote team with a global footprint).