Data Analysis Lead

Job Description This is an onsite role with minimum 4 days in office.
• Experience & Domain Knowledge: 6+ years of experience in data analytics or a related field, with a substantial portion in the telecommunications industry preferably in the networking experienced in data analysis/data scientist roles.
• Data Analysis Skills: Exceptional analytical and problem-solving skills. Advanced proficiency in SQL for querying large databases and Python for data analysis (pandas, numpy, etc.) and scripting. You can efficiently manipulate and analyze large datasets to extract meaningful insights.
• AI/ML & LLM Proficiency: Experience with machine learning or advanced analytics techniques. Exposure to AI/ML frameworks (such as scikit-learn or TensorFlow) and familiarity with Large Language Models (LLMs) or natural language processing is a big plus – you’re comfortable exploring new AI-driven approaches to glean insights from data (structured or unstructured).
• Data Visualization: Proficiency in creating clear and compelling dashboards and visualizations using Tableau or Power BI (or similar tools). You know how to tell a story with data, highlight key metrics, and make complex data understandable to non-technical stakeholders.
• Databricks & Big Data: Experience working with big data platforms like Databricks (or Spark) to perform distributed data processing and advanced analytics. Ability to optimize data workflows and handle large-scale data (e.g., streaming data from telecom networks or high-volume customer transaction data).
• Detail-Oriented & Quality-Focused: Demonstrated commitment to data quality and accuracy. Experience with data assurance practices, data governance, or QA in analytics projects – you ensure the insights you provide are rock-solid and reliable.
• Strategic Mindset: Ability to see the big picture and align analysis with business strategy. As a senior professional, you can prioritize analysis that drives strategic decisions and not just produce reports. You’re comfortable presenting to leadership and can translate data findings into strategic recommendations.
• Independent & Collaborative: Self-starter who can drive projects with minimal guidance, and a team player who collaborates well across departments. You can independently manage your workload and also work in tandem with others – for example, pairing with a data engineer to improve data pipelines or brainstorming with a product manager on what metrics best define success for a new initiative.
• Education: A Bachelor’s or Master’s degree in a relevant field (e.g., Data Science, Statistics, Computer Science, Engineering, or Business). Equivalent hands-on experience and certifications in analytics/AI are also considered.