Core Skills:• Proficiency in Python, SQL, Excel, and Power BI.
• Strong data analysis skills with the ability to quickly understand and interpret data.
• Expertise in reading and digesting dashboards and reports to generate actionable insights.
• Ability to present insights and conclusions effectively to senior leaders.
• Creative problem-solving skills using data, with a knack for developing quick and innovative solutions.
• Strong communication skills to present complex data and ideas in a simple, non-technical manner for easy understanding
AI & Advanced Analytics Expertise:• Deep understanding of current trends in AI and AI transformation.
• Ability to apply the latest AI tools and technologies to solve business problems.
• Technical knowledge of AI technologies with hands-on experience in developing AI applications, including Classical ML, Deep Learning, and Generative AI.
• Hands-on coding experience is highly desirable.
Preferred Qualifications:• Strong understanding of data warehousing (DW) concepts.
• Experience in Snowflake, Tableau, and DBT is a plus.
Skills: - AI/ML Expertise: Understanding of machine learning lifecycle, model deployment, and data pipelines.
- Agile & Scrum: Strong knowledge of Agile methodologies, sprint planning, backlog grooming, and working with cross-functional teams.
- Project & Program Management: Experience in managing AI/ML projects end-to-end, including risk management, stakeholder communication, and resource allocation.
- Technical Understanding: Familiarity with ML frameworks (TensorFlow, PyTorch), cloud platforms (AWS, GCP, Azure), and MLOps best practices.
- Collaboration: Working closely with data scientists, engineers, and business stakeholders to ensure AI projects align with strategic goals.
- Metrics & ROI Measurement: Defining key performance indicators (KPIs) for AI models and tracking their success post-deployment.
Responsibilities: - Leading AI/ML initiatives, ensuring they align with business objectives.
- Managing cross-functional teams to develop and deploy AI solutions efficiently.
- Facilitating Agile ceremonies (stand-ups, sprint reviews, retrospectives).
- Ensuring AI/ML models are continuously improved and integrated into business processes.
- Managing risks, dependencies, and compliance challenges in AI deployments.
- Driving innovation by exploring new AI trends and methodologies.