Description :

Experience : 7+ Years (Minimum 2 years in leadership)

Industry : Geospatial Intelligence / Data Science

Education : Bachelors or Masters degree in Statistics, Data Science, Business Analytics, or a related field.

Role Summary :

As the Assistant Vice President (AVP) of Data Analytics & Science at GeoIQ, you will be a strategic leader responsible for architecting the organizations data-driven roadmap.

You will manage a high-performing team of analysts and data scientists, bridging the gap between raw data extraction and high-level business outcomes.

This role demands a seasoned professional who can balance technical depthspecifically in statistical modeling and machine learningwith the business acumen required to develop cross-functional analytics strategies.

You will oversee the entire data lifecycle, from ensuring rigorous data governance and quality standards to integrating emerging technologies that keep GeoIQ at the forefront of the geospatial and behavioral analytics space.

Responsibilities :

- Strategic Leadership & Mentorship : Lead and mentor a dynamic team of analysts, fostering a culture of innovation while providing hands-on guidance on complex analytical methodologies and professional growth.

- Analytics Strategy Execution : Architect and execute a comprehensive analytics strategy that aligns with GeoIQs business objectives, ensuring all data initiatives provide a measurable return on investment.

- Advanced Statistical Modeling : Utilize advanced statistical techniques and machine learning models to identify trends and patterns within massive datasets, converting unstructured data into actionable business intelligence.

- Insights Extraction : Conduct deep-dive analyses across varied data sources to uncover opportunities for process optimization and new product development.

- Data Visualization & Storytelling : Oversee the design and delivery of impactful dashboards and reports, ensuring complex data findings are translated into clear, visual narratives for both technical and non-technical stakeholders.

- Data Quality & Governance : Establish and enforce strict data quality standards and governance protocols to ensure the accuracy, reliability, and security of all analytical outputs.

- Infrastructure Optimization : Stay current with emerging AI and analytics tools, recommending and implementing upgrades to the existing technology stack to improve computational efficiency.

- Stakeholder Engagement : Act as a key consultant to cross-functional heads, identifying their data requirements and providing the insights necessary for informed, evidence-based decision-making.

Technical Requirements :

- Core Experience : 7+ years of experience in data analysis and science, with a proven track record of at least 2 years in a managerial or team-lead capacity.

- Programming Mastery : Expert proficiency in Python or R for data manipulation, statistical analysis, and predictive modeling.

- Data Querying : Advanced SQL skills for extracting and transforming data from complex relational and non-relational databases.

- Visualization Stack : Hands-on experience with enterprise visualization tools such as Tableau or Power BI.

- Machine Learning & Statistics : Deep understanding of statistical concepts (Regression, Hypothesis Testing) and machine learning frameworks (Scikit-Learn, XGBoost, etc.

- Geospatial Context : (Preferred) Experience or familiarity with geospatial data analysis and location intelligence frameworks.

Preferred Skills :

- Innovative Problem Solving : Ability to design novel analytical approaches to solve non-linear business challenges.

- Communication Excellence : Exceptional ability to present complex data findings to executive leadership in a persuasive and easy-to-digest manner.

- Adaptability : Thriving in a fast-paced, high-growth environment where technology and data sources evolve rapidly.

- Business Acumen : Strong focus on leveraging data to drive specific business outcomes such as revenue growth, churn reduction, or operational efficiency.

- Tech Integration : Familiarity with cloud data warehouses (e., Snowflake, BigQuery) and MLOps principles