Lead Data Analyst

Role Overview: As a Lead Data Analyst at Ascentt, you will be responsible for leading the end-to-end analytics lifecycle for cost-focused projects. Your role will involve defining key objectives, delivering insights, and recommendations to senior stakeholders, and contributing to analytics initiatives aligned with business goals. You will play a crucial role in data strategy, KPI development, insights delivery, advanced analysis, data governance, data exploration, collaboration across teams, ensuring data quality, and contributing to team growth. Key Responsibilities: - Lead the end-to-end analytics lifecycle for cost-focused projects, from defining key objectives to delivering insights and recommendations to senior stakeholders. - Define and implement robust data models for ensuring scalability and accuracy of cost metrics for reporting and forecasting. - Design, measure, and monitor cost-related KPIs such as cost-per-unit, cost-of-service, budget utilization, and return on investment (ROI). - Create dashboards and reports to effectively communicate cost metrics and trends to stakeholders for enabling data-driven decisions. - Conduct exploratory data analysis, trend forecasting, and scenario modeling to identify cost-saving opportunities and potential risks. - Ensure data quality, integrity, and compliance by working closely with data engineering and governance teams. - Analyze datasets to uncover trends, patterns, and insights that help the business better understand cost dynamics. - Work closely with Finance, Operations, and other departments to align analytics with organizational needs and goals. - Partner with data engineers and team members to ensure the accuracy and reliability of data. - Share knowledge and insights with team members to foster a collaborative and innovative work environment. Qualifications: - Bachelors or Masters degree in Data Science, Economics, Finance, Statistics, or a related field. - 8+ years of experience in data analytics, with expertise in cost analytics, financial modeling, and cost optimization. - Proficiency in data analysis tools and languages such as SQL, Python, or R. - Hands-on experience with BI tools like Tableau, Power BI, or Looker. - Strong understanding of database systems, data warehousing, and ETL processes. - Analytical mindset with the ability to transform complex data into actionable insights. - Excellent written and verbal communication skills, with experience presenting findings to stakeholders. - Ability to manage multiple priorities and deadlines in a fast-paced environment.,