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 provide business recommendation to the business
• 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. For example, analyzing sales data from the past year to identify growth trends or seasonal variations.
   • Diagnostic Analytics: A deeper dive into data to understand the cause behind a trend or anomaly. It answers questions like "Why did sales drop last quarter?"
   • Predictive Analytics: Uses statistical models and machine learning to predict future trends or behaviors based on historical data. For example, forecasting customer demand or sales revenue.
   • Prescriptive Analytics: Recommends actions based on data analysis. It goes beyond prediction to suggest the best course of action to optimize outcomes. For example, recommending inventory levels or marketing strategies based on demand predictions.

4. Data Visualization:

   • Visualization tools like Tableau, Power BI, and QlikView help in presenting data insights in an easily digestible format, such as charts, graphs, dashboards, and heatmaps.
   • 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, classify data, and make predictions. These techniques can handle large volumes of data and adapt to new data as it becomes available.
   • Text Analytics: Involves extracting insights from unstructured textual data (e.g., social media posts, customer reviews, chat logs). Techniques include sentiment analysis and topic modeling.
   • Big Data Analytics: Handles vast amounts of data that traditional analytics tools cannot process efficiently. Tools like Hadoop, Spark, and Google BigQuery are used to manage and analyze large datasets.

6. Key Metrics & KPIs:

   • Businesses track key performance indicators (KPIs) to measure the success of their products, services, and strategies. Examples of KPIs include customer retention rate, conversion rate, revenue growth, net promoter score (NPS), and market share.
   • 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 for analyzing and manipulating datasets.
   • Data Visualization: Expertise in visualization platforms like Tableau, Power BI, and Google Data Studio to communicate data insights visually.
   • Statistical Analysis: Knowledge of statistical methods and techniques such as hypothesis testing, regression analysis, and time series forecasting.
   • Machine Learning & AI: Familiarity with machine learning algorithms and frameworks (e.g., scikit-learn, TensorFlow, Keras) to build predictive models.
   • Big Data Technologies: Familiarity with platforms like Hadoop, Spark, or AWS for analyzing large datasets.

2. Business & Analytical Skills:

   • Problem Solving: The ability to approach complex business problems and break them down into manageable analytical tasks.
   • Data Interpretation: Ability to interpret the results of data analysis and translate them into actionable insights that drive business outcomes.
   • Strategic Thinking: Understanding how data insights align with and support business goals, and using that knowledge to advise stakeholders on decisions.
   • Communication: Ability to communicate complex data insights clearly to non-technical stakeholders through reports, presentations, and discussions.