Data Analyst (ML)

About BLeader

BLeader is a leading Data & AI consulting and implementation company, founded in 2014. We specialize in guiding organizations through their data journey, from strategy and infrastructure to advanced AI solutions, with a focus on delivering measurable business results. As part of the Octally Group and a strategic partner of KPMG, we bring broad capabilities and enterprise-grade credibility to every project.

We work with cutting-edge technologies including Microsoft Fabric, Snowflake, Databricks, Azure OpenAI, and Anthropic Claude, serving clients like Shufersal, Papaya Global, AudioCodes, and OneZero.

We're looking for a Data Analyst with ML capabilities to join a newly formed data unit at one of our key clients. This is a unique opportunity to be a foundational member of a team that is building analytics and ML capabilities from scratch — meaning you'll have real impact on how things are shaped, not just how they're maintained.

This role is ideal for someone who has strong analytical skills and a solid foundation in ML, and is eager to grow deeper into the ML/AI space. You won't just run queries — you'll help define how data drives decisions across the organization.

• Analyze complex datasets to generate actionable business insights and support data-driven decision-making
• Design and build dashboards, reports, and data visualizations for stakeholders
• Develop and maintain ETL processes and data pipelines
• Build and deploy foundational ML models (classification, regression, clustering, forecasting)
• Collaborate with business stakeholders to translate business questions into analytical frameworks
• Help establish best practices, workflows, and standards for the data team as it scales
• Contribute to building the team's ML infrastructure and model lifecycle processes

Must-Have:

• 2+ years of experience as a Data Analyst or similar analytical role
• Strong proficiency in SQL and Python (Pandas, NumPy, Matplotlib/Seaborn)
• Experience with BI tools (Tableau, Power BI, Looker, or similar)
• Foundational knowledge of ML concepts and hands-on experience with Scikit-learn or equivalent
• Solid understanding of statistics and data modeling
• Ability to communicate insights clearly to both technical and non-technical audiences
• A proactive, self-starter mindset — you'll be building, not just maintaining

Nice-to-Have:

• Experience with cloud platforms (AWS, GCP, or Azure)
• Familiarity with MLOps tools or ML deployment pipelines
• Experience working with large-scale or unstructured data
• Background in a consulting or client-facing environment