Data Insights Manager

Roles and responsibilities

• General: This role is a blend of hands-on ‘in the business’ and executive level ‘on the business’ work. To succeed you will enjoy rolling up your sleeves, leading a team and contributing to strategy.
• Project Delivery: Manage, standardize & validate the structure of business cases for product development, headcount, engineering resources, budgets, general strategic & roadmap prioritization.
• Experimentation: Define success, measure/validate experiments and help ingrain an experimental mindset within the teams.
• Forecasting and Insights: Provide a macro-led business intelligence view to the organization to prevent missed opportunities, surmount obstacles at all organizational levels and drive commercial behaviors.
• Analytics & Alerts: Quantitative analysis, data mining and presentation of business metrics; identify drivers and build an end-to-end communication framework based on business value, effort and urgency.
• Process Improvement: Work with all teams to drive inter- and intra-departmental efficiencies, optimize processes and prioritize system enhancements.
• Reporting: Build dashboards, internal/external reports and present key datasets to enable all team members to efficiently and effectively monitor performance and prioritize their efforts.
• Leadership: Lead a small team of experts to deliver the above functions and be excited by the challenge of mentoring team members across the chain and connectivity teams.

What you’ll Need to Succeed:

• 6+ years of leadership experience in analytics/data science/insights/strategy.
• 3+ years’ experience leading analytics, operational, product or other technical teams.
• Expert domain of data analysis and data visualization tools and software such as Excel, SQL, Tableau, Python, R, or similar.
• Strong statistical modelling and machine learning knowledge.
• Strong experience in finding data insights and providing business recommendations.
• Excellent communicator with superior written, verbal, presentation and interpersonal communication skills.
• Ability to multi-task, prioritize and coordinate resources.
• Strong program/project management experience.
• Bachelor’s degree ideally in a business or quantitative subject (e.g. computer science, mathematics, engineering, science, economics or finance).
• Experience in articulating strategic issues and negotiating with C-level executives – experience in leading strategy consulting projects a plus.
• People management – track record of developing stars.
• Ability and willingness to drive projects independently, working efficiently to deliver results rapidly and engaging the relevant stakeholders throughout the process.

It’s Great if you Have:

• Master’s degree in statistics, economics, mathematics or similar discipline.
• Experience in conducting A/B testing experimentation.
• Travel industry/e-commerce/tech/consulting experience.

Desired candidate profile

1. Data Collection:

   • Data Sources: Data can come from a variety of sources, such as internal databases, CRM systems, web analytics platforms, surveys, social media, IoT devices, and external data providers.
   • Structured vs. Unstructured Data: Structured data is highly organized (e.g., tables in relational databases), while unstructured data (e.g., emails, text files, images) requires more complex analysis techniques.

2. Data Cleaning and Preparation:

   • Before analysis, data must be cleaned to ensure accuracy and consistency. This involves handling missing values, removing duplicates, and normalizing data formats.
   • Data preparation often includes aggregating, transforming, or combining multiple data sources to create a unified dataset for analysis.

3. Data Analysis:

   • Descriptive Analytics: Involves summarizing historical data to understand trends and patterns.
   • Diagnostic Analytics: A deeper dive into data to understand the cause behind a trend or anomaly.
   • Predictive Analytics: Uses statistical models and machine learning to predict future trends or behaviors based on historical data.
   • Prescriptive Analytics: Recommends actions based on data analysis.

4. Data Visualization:

   • Visualization tools like Tableau, Power BI, and QlikView help in presenting data insights in an easily digestible format.
   • Effective data visualization helps stakeholders quickly grasp key insights and make informed decisions.

5. Advanced Analytics Techniques:

   • Machine Learning and AI: Used to build models that can automatically detect patterns in data.
   • Text Analytics: Involves extracting insights from unstructured textual data.
   • Big Data Analytics: Handles vast amounts of data that traditional analytics tools cannot process efficiently.

6. Key Metrics & KPIs:

   • Businesses track key performance indicators (KPIs) to measure the success of their products, services, and strategies.
   • Identifying the right KPIs is crucial for aligning data insights with organizational goals.

Skills for Data Insights & Analytics

1. Technical Skills:

   • Data Analysis Tools: Proficiency in tools such as SQL, Excel, Python, and R.
   • Data Visualization: Expertise in visualization platforms like Tableau, Power BI, and Google Data Studio.
   • Statistical Analysis: Knowledge of statistical methods and techniques.
   • Machine Learning & AI: Familiarity with machine learning algorithms and frameworks.
   • Big Data Technologies: Familiarity with platforms like Hadoop, Spark, or AWS.

2. Business & Analytical Skills:

   • Problem Solving: The ability to approach complex business problems.
   • Data Interpretation: Ability to interpret the results of data analysis.
   • Strategic Thinking: Understanding how data insights align with business goals.
   • Communication: Ability to communicate complex data insights clearly.