Senior Data Analyst (Courier & Logistics)

Key Skills

Data Analytics, Data science techniques, Looker, Machine learning techniques, MATLAB Programming, Mixpanel, Python Programming, SQL, Tableau

Job Description

Analytics at Wolt is a business-critical competence team that covers business intelligence, data science, and data analytics. In total, we are a team of 50+ professionals working from Finland, Sweden, Germany, and Estonia all united by our excitement to put Wolt’s valuable data assets into everyday use.

Now we are looking for a Senior Data Analyst to join us in the Courier & Logistic Analytics team in Helsinki, Berlin, or Stockholm.

A look into the Data Analytics at Wolt:

Data analysts first and foremost strive to gain actionable insights from all available data that support the decision-making in the teams they work with. In other words, data analysts are expected to be the objective advisors who base their findings and recommendations on data and solid data analysis methods. 

Data analysts at Wolt work as embedded in a team on one particular domain at a time. This means they are working as close as possible where the decisions are made (i.e. in product development teams or marketing or finance etc.) and hence get to participate in decision-making themselves. In this particular role, you would get to collaborate closely with the Product and Operations teams within the Courier and Logistics group. The Courier and Logistics group’s mission at Wolt is to build an efficient logistics platform and provide a great delivery experience for the couriers. 

As a Senior Data Analyst at Wolt, you’ll get to:

• Step up to the challenge and take ownership of the data analytics topics within Courier & Logistics.
• Empower more junior data analysts, mentoring them to tackle complex data analysis challenges.
• Be the driving force behind the insights that help our business and product teams comprehend Wolt's marketplace dynamics and anticipate long-term trends.
• Contribute to product development by sharing recommendations that make a real impact, based on statistical techniques in Python, R or similar.

• Will I have to build dashboards? You can go with Python/R notebooks and presentations only but our Looker platform is at the moment the handiest way to share your (scheduled to refresh) results to a bigger audience. We do think traditional BI dashboards are great in monitoring our business and benefit data analysts with automating data visualization work and inspiring new hypotheses/questions. 
• Will I spend 80 % or more of my time in data plumbing/wrangling? Analytics Teams at Wolt have both Data Analysts and BI Developers. BI Developers ensure that we gather relevant data and make it accessible in a scalable and trustworthy manner for the Data Analysts. This is to enable the Data Analysts to work more efficiently within the cross-functional Operations and Product teams, helping them to focus on true data analysis and make Wolt truly data-driven by exploratory and evaluative analytics.
• I want to use machine learning methods in my work. Should I pursue the data science path? Many data analysts at Wolt do utilize ML methods in their work. The main difference between a model built by a data scientist and a data analyst is the audience, purpose, and scale of the model. A model by a data analyst should support the product development and decision-making of stakeholders (insights that people can discuss) whereas a model by a data scientist is expected to be deployed and do millions of decisions as part of our product (think personalization or time estimates in our consumer app). So if ML models for you are (just) tools that assist in generating insights then being a data analyst is definitely the path for you. 
• I’ve never worked in a tech company and done A/B testing. Is this a blocker for me in becoming a data analyst? Indeed as we are curious people and have digital products we often conduct controlled experiments (A/B tests, Switchback experiments, etc). Running reliable experiments is challenging and this is why a solid grasp of statistics, particularly within DOE, is necessary to truly thrive in this role.

Degree in Mathematics, Statistics, Computer Science, or a related field.

• 5+ years of experience in a data science or analytics position within an industry or research setting.
• Expertise in statistical analysis, including hypothesis testing, experimentation, and regression analysis using statistical software packages in Python, Matlab, R, or equivalent.
• Proficiency in writing structured and efficient SQL queries for large data sets.
• Have prior experience building intuitive data visualizations and dashboards that influence business decisions, utilizing tools like Tableau, Mixpanel, Looker, or similar.
• Relentless pursuit of new ideas and data sources, and have the ability to systematically tackle ambiguous problems with a data-driven, hypothesis-based approach.