Chief Data Analytics Manager

The Senior Business Analyst will lead and manage the analysis of data to drive business decisions across multiple projects.

This role involves advanced statistical expertise, proficiency in analytics tools, and the ability to communicate insights effectively to product and research teams.

Key Responsibilities:

• Conduct relevant statistical tests to validate hypotheses and ensure data-driven outcomes.
• Design, develop, and deploy advanced statistical and machine learning models to analyze complex datasets.
• Perform data fusion activities by leveraging techniques to merge, reconcile, and analyze information from disparate systems and formats.
• Employing modern weighing and projection methods to answer key business questions and predict future trends.
• Design and manage interactive dashboards using Power BI for real-time data visualization.
• Perform Exploratory Data Analysis (EDA) to identify patterns, anomalies, and opportunities.
• Collaborate with product and research teams by providing feedback based on analytical findings.
• Analyze survey data to extract actionable insights and trends.
• Define and create Key Performance Indicators (KPIs) based on APP data.
• Serve as the gatekeeper for data maps, ensuring data integrity and accessibility for stakeholders.
• Develop and maintain MIS dashboards.

Required Skills & Qualifications:

• Data Analysis & Statistical Expertise: Ability to manipulate large datasets, perform data cleaning, and run relevant statistical tests to validate findings and ensure data accuracy.
• Proficiency in Data Visualization Tools: Advanced skills in Power BI to design, develop, and maintain interactive, user-friendly MIS dashboards with features like advanced calculations, parameters, joins, and blending.
• Advanced Excel Skills: Expertise in Excel formulas, pivot tables, macros, and dashboard creation to complement BI tools and automate reporting tasks.
• Programming Knowledge: Familiarity with Python for data manipulation, exploratory data analysis (EDA), and predictive modelling enhances analytical capabilities.
• Data Validation & Quality Assurance: Skill in validating data integrity and consistency to ensure reliable dashboard outputs.
• Analytical & Business Skills Thinking & Problem Solving: Strong analytical mindset to interpret complex data, identify trends, and provide actionable insights.
• Business Acumen: Understanding of the business context to translate data insights into relevant recommendations and feedback for product and research teams.
• Communication & Collaboration Communication: Ability to present complex data insights clearly and succinctly to internal stakeholders, including senior executives, through reports, dashboards and presentations.
• Stakeholder Management: Experience collaborating with cross-functional teams, gathering requirements, and incorporating feedback to refine dashboards and reports.
• Coaching & Mentoring: Mentor and guide junior data scientists and analysts, fostering a culture of continuous learning and innovation.

Benefits:

• Experience working with app-based data and multi-project analytics environments.
• Familiarity with analyzing multiple survey data.
• Background in creating and managing KPIs aligned with business objectives.
• Exposure to big data technologies and cloud-based data platforms.
• Ability to work collaboratively in cross-functional teams involving product, research, and engineering stakeholders.
• Must have led the data science team in the past.
• Experience of developing algorithms using a combination of data science and statistics.

Others:

• MSc in Statistics.
• Strong experience in statistical analysis, predictive modeling, and data validation.
• Proficiency in Python and Power BI is necessary, with experience of working in libraries used for numerical and text analysis such as Pandas, Numpy, PySpark, NLTK, SpaCy, Scikit-Learn, Genism.
• Experience of working with both parametric and non-parametric methods.
• Advanced knowledge of survey analytics and EDA techniques.
• Excellent communication skills for cross-functional collaboration.
• Experience with MIS dashboard development and KPI creation.
• High attention to detail and commitment to data quality.