Customer Insight Data Analyst:

Responsibility:

• Analyzing customer data captured through different methods, like loyalty schemes, and delivering actionable insight helps drive business decisions and allows us to serve our customers better.
• Delivering compelling insight into the behaviour of our customers to the wider business
• Providing ad hoc customer analysis at speed to answer strategic business questions
• Regular reporting of customer metrics, highlighting key contributing factors to change
• Automating the creation of aggregated data tables to be used in regular reporting
• Creating and maintaining data products that enable stakeholders to self-serve data
• Investigating the impact that major events and deals have on customer behaviour
• Creating advanced mathematical models to understand and predict customer behaviour

Skills & Experience:

• Coding in SQL, Python etc
• Use of data visualisation techniques like Power BI, Tableau, Matplotlib, Seaborn etc
• Creating and deploying machine learning models
• Communicate complex data in a clear and concise way
• Data engineering and data architecture principles
• Product and project management techniques

Data Scientist Skills:

A data scientist needs a mix of technical, analytical, and soft skills to excel in the role. Here’s a comprehensive list of skills:

Technical Skills

1. Programming
   • Proficiency in Python, R, or Julia for data analysis and modeling.
   • Knowledge of SQL for database querying.
2. Data Manipulation and Analysis
   • Expertise in pandas, NumPy, and dplyr for data manipulation.
   • Experience with data wrangling and cleaning techniques.
3. Statistical Analysis
   • Strong foundation in statistics (hypothesis testing, regression, probability distributions).
   • Familiarity with Bayesian methods and A/B testing.
4. Machine Learning
   • Understanding of supervised, unsupervised, and reinforcement learning.
   • Experience with libraries like scikit-learn, TensorFlow, or PyTorch.
5. Data Visualization
   • Ability to create visualizations using matplotlib, seaborn, ggplot2, or Tableau.
   • Storytelling with data to communicate insights.
6. Big Data Tools
   • Knowledge of Spark, Hadoop, or other distributed computing platforms.
7. Cloud Computing
   • Familiarity with AWS, Azure, or Google Cloud for deploying models and storing data.
8. Data Engineering Basics
   • Understanding ETL pipelines, APIs, and data integration processes.

Analytical Skills

1. Problem-Solving
   • Ability to frame business problems as data science questions.
2. Critical Thinking
   • Evaluating data integrity, bias, and implications of models.
3. Mathematics
   • Proficiency in linear algebra, calculus, and optimization techniques.

Soft Skills

1. Communication
   • Explaining complex technical concepts to non-technical stakeholders.
2. Collaboration
   • Working effectively with cross-functional teams (engineers, product managers, etc.).
3. Adaptability
   • Learning and applying new tools or methods as technology evolves.
4. Project Management
   • Managing timelines and deliverables effectively in projects.

Business Acumen

1. Domain Knowledge
   • Understanding the specific industry (e.g., healthcare, finance, retail) to tailor solutions.
2. Strategic Thinking
   • Connecting insights to business goals and decision-making.