Lead Data Analyst

As a Lead Data Analyst at Ascentt, you will be responsible for leading the end-to-end analytics lifecycle for cost-focused projects. This includes defining key objectives, delivering insights, and making recommendations to senior stakeholders. Your role will involve contributing to analytics initiatives with a focus on cost metrics to ensure alignment with business goals. You will be tasked with defining and implementing robust data models to ensure the scalability and accuracy of cost metrics for reporting and forecasting. Additionally, you will design, measure, and monitor cost-related Key Performance Indicators (KPIs) such as cost-per-unit, cost-of-service, budget utilization, and return on investment (ROI). Your responsibilities will also include creating dashboards and reports that effectively communicate cost metrics and trends to stakeholders, enabling data-driven decisions. In this role, you will conduct advanced analysis through exploratory data analysis, trend forecasting, and scenario modeling to identify cost-saving opportunities and potential risks. You will collaborate closely with data engineering and governance teams to ensure data quality, integrity, and compliance. Furthermore, your role will involve analyzing datasets to uncover trends, patterns, and insights that help the business better understand cost dynamics. Collaboration across teams is essential, as you will work closely with Finance, Operations, and other departments to align analytics with organizational needs and goals. You will partner with data engineers and other team members to ensure the accuracy and reliability of data. Additionally, you will share knowledge and insights with team members to contribute to team growth and foster a collaborative and innovative work environment. To qualify for this role, you should hold a Bachelors or Masters degree in Data Science, Economics, Finance, Statistics, or a related field. You should have 8+ years of experience in data analytics, with demonstrated expertise in cost analytics, financial modeling, and cost optimization. Proficiency in data analysis tools and languages such as SQL, Python, or R is required, along with hands-on experience with BI tools like Tableau, Power BI, or Looker. A strong understanding of database systems, data warehousing, and ETL processes is essential. You should possess a strong analytical mindset with the ability to transform complex data into actionable insights. Excellent written and verbal communication skills are necessary, with experience in presenting findings to stakeholders. The ability to manage multiple priorities and deadlines in a fast-paced environment is also crucial for success in this role.,