Business Analytics Lead Analyst

Key Skills: AI/ML, Forecasting, SQL, Python, Budgeting, Predictive Analysis, Cloud (AWS / Azure / GCP)

Roles and Responsibilities:

Core Technical Skills

• Develop and implement solutions using Python, machine learning, and AI engineering.
• Apply GenAI, LLMs, small language models, RAG, and agentic AI to business use cases.
• Design, develop, and tune NLP and predictive models.
• Build and manage data workflows on platforms such as Databricks or similar platforms.
• Apply strong prompt engineering fundamentals to AI and GenAI solutions.

Problem-Solving and Business Skills

• Translate business needs into effective technical solutions, focusing on measurable business outcomes.
• Work with Finance datasets and ERP-related data.
• Apply curiosity, adaptability, and a problem-solving mindset to develop new solutions.
• Partner with engineering teams and AI CoEs to productize analytics and AI/ML models.

Ways of Working

• Start small, iterate quickly, and continuously improve solutions based on feedback.
• Collaborate directly with global Finance stakeholders.
• Work effectively in global environments with empathy for end users.
• Build and experiment with new AI capabilities and solutions.
• Contribute to shaping analytics and AI roadmaps and initiatives.

Skills Required:

• 5-8 years of experience in analytics, data science, or business intelligence, with at least 5 years in a leadership or lead role.
• Experience delivering analytics or automation initiatives for Finance or FP&A functions.
• Knowledge of finance data domains such as general ledger, allocations, forecasting, and planning.
• Strong skills in Power BI, Databricks, Microsoft Fabric, and Denodo.
• Strong hands-on experience with Python, machine learning, and AI engineering.
• Experience with GenAI, LLMs, small language models, RAG, and agentic AI.
• Experience with NLP, predictive analytics, forecasting, and machine learning models.
• Familiarity with AI/ML frameworks and tools such as Python, TensorFlow, and Azure ML.
• Experience working with Finance datasets or ERP-related data.
• Experience partnering with engineering teams and AI CoEs to productize models.
• Strong communication and stakeholder management skills.
• Ability to work effectively with global Finance stakeholders and distributed teams.
• Strong problem-solving, analytical, and business-oriented mindset.
• Ability to work independently, experiment, iterate, and develop solutions in an evolving environment.

Good to Have

• Certifications such as Azure AI Engineer or Data Science Professional.
• Experience with emerging GenAI and automation tools.
• Experience building AI capabilities and analytics solutions for Finance functions.

Education: Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Business Analytics, Finance, or a related field.