Analytics Specialist

Roles and responsibilities

Create and drive Value Added Service concepts on the basis of customer and market requirements to meet industry standards.

Develop strong, value-add partnerships and managing 3rd party companies.

Work with in country teams to create development plans for new product or service development, and execute project plans for the launch of new features, incorporating merchandising and promotion strategies

Manage pilot projects to test new services and roll out negotiated services across the country, continuously improve our service offering

Have a thorough understanding of the product category, seasonality and continually monitor customers feedback and vendors developments

A day in the life

You’ll be based at one of our sites. Being on site allows you to stay close to every part of the delivery process and means you have visibility of everything to guide our logistics partners. You’ll look after all elements of our partner relationships, from getting the contracts in place to keeping track of performance.

This role is all about clear communication. You’ll be in daily contact with the partners you manage to make sure they have everything they need to carry out deliveries on time.

About The Team

Amazon Logistics, or AMZL, handles ‘last mile’ delivery duties in partnership with third-party distribution businesses. We utilize creative thinking and continuous improvement initiatives to get millions of physical products into the hands of our customers. Our goal is to make our customers’ delivery experience as smooth as possible and roll out global delivery solutions for our newest concepts.

Basic Qualifications

• Advance SQL skills are mandatory
• 3+ years of developing, negotiating and executing business agreements experience
• 3+ years of product or program management or business development
• Experience interpreting data and making business recommendations
• Experience driving roadmap strategy and on-ground implementation navigating across stakeholders
• Experience working across functional teams and senior stakeholders
• Effective email writing and communication skills
• Intermediate excel and data analysis skills

Desired candidate profile

1. Data Analysis and Statistical Skills

• Proficiency in using statistical methods to analyze and interpret data sets.
• Ability to apply techniques such as regression analysis, hypothesis testing, and predictive modeling to solve business problems.
• Strong understanding of statistical software and tools (e.g., R, SAS, SPSS).

2. Data Visualization

• Expertise in creating visual representations of data through graphs, charts, and dashboards.
• Proficiency in data visualization tools like Tableau, Power BI, or Google Data Studio to present insights clearly and effectively.

3. Data Management and Cleaning

• Ability to clean, preprocess, and organize raw data to ensure it is accurate and usable for analysis.
• Experience with data wrangling and using tools like SQL, Excel, or Python for data manipulation.

4. Advanced Excel Skills

• Expertise in using Excel functions (e.g., VLOOKUP, pivot tables, macros, and data analysis toolpak) to process and analyze large datasets.
• Knowledge of advanced Excel features for automation and data visualization.

5. Business Acumen

• Understanding of how analytics can influence business strategies and operations.
• Ability to align data analysis efforts with business objectives and priorities, ensuring actionable insights that drive value.

6. Problem-Solving and Critical Thinking

• Strong analytical thinking skills to approach complex business problems with a data-driven mindset.
• Ability to break down complex problems, identify the core issue, and recommend data-driven solutions.

7. Knowledge of Databases and Querying

• Proficiency in SQL or other querying languages to extract and manipulate data from relational databases.
• Familiarity with database management systems (e.g., MySQL, PostgreSQL, Microsoft SQL Server).

8. Programming Skills

• Knowledge of programming languages, such as Python or R, to automate data analysis tasks, run statistical models, and develop custom solutions.
• Familiarity with data analysis libraries and frameworks (e.g., Pandas, NumPy, SciPy, Matplotlib for Python).

9. Machine Learning and Predictive Modeling (Optional)

• Understanding of machine learning techniques and how they can be used to predict future trends or behaviors based on historical data.
• Experience with tools like Scikit-learn, TensorFlow, or Keras to build predictive models (for more advanced positions).