Lead Data Scientist-Vice President

Location: Chennai, Tamil Nadu, India

Employment Type: Regular

The Applications Development Technology Lead Analyst is a senior level position responsible for establishing and implementing new or revised application systems and programs in coordination with the Technology team. The overall objective of this role is to lead applications systems analysis and programming activities.

Responsibilities

• Strategic Leadership & Vision: Define and execute the data analytics strategy, aligning it with overall business objectives and identifying opportunities for leveraging data to gain competitive advantage.
• Project & Program Management: Lead and oversee multiple complex data analytics projects, ensuring timely delivery, high quality, and measurable business impact. Take full ownership of analytical initiatives from problem definition to solution implementation and post-launch evaluation.
• Advanced Analytics & Modeling: Apply sophisticated statistical analysis, predictive modeling, machine learning techniques, and experimental design (A/B testing) to uncover deep insights, forecast trends, and assess the impact of business decisions.
• Stakeholder Engagement & Communication: Collaborate effectively with senior leadership, cross-functional department heads, and external partners to understand business challenges, articulate analytical solutions, and communicate complex findings and recommendations in a clear, concise, and compelling manner.
• Team Leadership & Mentorship: Provide strong technical and career mentorship to a team of data analysts, fostering their growth, developing their skills, and promoting best practices in data governance, data quality, and analytical methodology.
• Data Architecture & Governance: Partner with data engineering and IT teams to influence data architecture design, ensure data quality, accessibility, and integrity, and establish robust data governance frameworks.
• Tooling & Innovation: Evaluate and recommend new analytical tools, technologies, and methodologies to enhance the team's capabilities and drive continuous innovation in data analytics.
• Performance Monitoring & Optimization: Design, develop, and implement advanced dashboards, reporting systems, and analytical frameworks to monitor key business performance indicators, identify root causes of performance issues, and recommend optimization strategies.

Qualifications

Required Skills & Experience

• Bachelor's degree in a quantitative field such as Computer Science, Statistics, Mathematics, Engineering, Economics, or a closely related discipline. A Master's degree or MBA is highly preferred.
• Minimum of 13+ years of progressive experience in data analytics, business intelligence, data science, or a similar data-intensive role, with at least 3-5 years in a leadership or lead analyst capacity.
• Expert-level SQL Proficiency: Demonstrated mastery in writing and optimizing complex SQL queries for data extraction, transformation, and analysis across large-scale data warehouses and databases.
• Advanced Programming Skills: Expert proficiency in at least one major programming language for data analysis (e.g., Python, R), with extensive experience using relevant libraries (e.g., Pandas, NumPy, Scikit-learn, SciPy in Python; tidyverse, ggplot2, caret in R).
• Business Intelligence & Visualization Expertise: Proven track record of designing and implementing executive-level dashboards, interactive reports, and compelling data visualizations using industry-leading BI tools (e.g., Tableau, Power BI, Looker, Qlik Sense) to drive strategic decisions.
• Statistical & Modeling Acumen: Deep understanding and practical application of statistical modeling, hypothesis testing, regression analysis, time series forecasting, and other advanced analytical techniques.
• Cloud Data Platforms: Extensive hands-on experience with cloud-based data platforms and services (e.g., AWS Redshift, S3, Athena, EMR; Google BigQuery, Cloud Dataflow, Cloud Storage; Azure Synapse Analytics, Data Lake) for data storage, processing, and analysis.
• Leadership & Strategic Thinking: Demonstrated ability to lead analytical initiatives, manage complex projects, mentor junior team members, and influence strategic decision-making at a senior organizational level.
• Exceptional Communication: Superior ability to communicate complex technical concepts and analytical insights to both highly technical and non-technical executive audiences through clear, concise, and impactful presentations and written reports.
• Problem-Solving & Critical Thinking: Outstanding analytical and problem-solving skills, with a proven ability to identify root causes, develop innovative solutions, and drive continuous improvement.

Preferred Skills & Experience

• Master's degree or PhD in a quantitative field, or an MBA with a strong quantitative focus.
• Experience in a relevant industry such as financial services, technology, or consulting.
• Proven experience in defining, implementing, and managing data governance policies and procedures.
• In-depth knowledge and practical experience with machine learning (ML) model development, deployment, and MLOps practices.
• Experience with big data technologies such as Hadoop, Spark, or Kafka.
• Familiarity with data orchestration tools and ETL/ELT processes.
• Relevant certifications in cloud platforms (e.g., AWS Certified Data Analytics, Google Cloud Professional Data Engineer) or specific data tools.

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