pracaon.plpracaon.pl

Data Scientist (Consumer Data)

Poznań, Polska, 61-569
Allegro
Partner
7d
Wynagrodzenie do ustalenia
Pełny etat • Hybrydowa • IT, Data i AI

Najważniejsze cechy oferty

  • Min. 2 lata doświadczenia

  • Data: SQL / BI / Python

  • Backend: Java / .NET / Node / Python

  • Model hybrydowy - część pracy zdalnie

  • Pełny etat

Job Description:

  • In the Consumer domain, we create and maintain customer-facing applications that help millions of clients each day to complete their purchases. We are looking for a mid-level Data Scientist to join our team and help move our search engine beyond simple keyword matching toward a Domain-Aware Hybrid Search (integrating LLMs, Semantic Search, and Knowledge Graphs). In this role, you will be responsible for supporting the development of the Central Query Understanding mechanism and implementing Predictive & Assistive Discovery to reduce the "cost of search" through automated intent recognition.

#Goodtobehere means that:

  • You will join a team you can count on - we work with top-class specialists who have knowledge- and experience-sharing in their DNA.

  • You will love our level of autonomy in team organization, the space for continuous development, and the opportunity to try new things.

  • You get to choose which technology solves the problem and you are responsible for what you create.

  • You will value our Developer Experience and the full platform of tools and technologies that make creating software easier. We rely on an internal ecosystem based on self-service and widely used tools such as Kubernetes, Docker, Consul, GitHub, and GitHub Actions. Thanks to this, you can contribute to Allegro from your very first days on the job.

  • You will be equipped with modern AI tools to automate repetitive tasks, allowing you to focus on developing new services and refining existing ones (also leveraging AI support).

  • You will create solutions that will be used (and loved!) by your friends, family and millions of our customers.

  • You will meet the Allegro Scale, which starts with over 1000 microservices, an open-source data bus (Hermes) with 300K+ rps, a Service Mesh with 1M+ rps, tens of petabytes of data, and production-used machine learning.

  • You will become part of Allegro Tech - We speak at industry conferences, cooperate with tech communities, run our own blog (it's been over 10 years!), record podcasts, lead guilds, and we organize our own internal conference - the Allegro Tech Meeting. We create solutions we love (and can) to talk about!

  • Send us your CV and... see you at Allegro!

We are looking for people with:

  • Have a degree strongly related to statistical/mathematical modeling.

  • Possess at least 2 years of experience in data analysis and building machine learning solutions that have been released to production.

  • Have a strong understanding of statistical and machine learning methods, specifically for forecasting and decision tree-based algorithms.

  • Are proficient in Python and efficient in using basic development tools.

  • Can process massive datasets (terabytes of data) using Google Cloud Platform solutions, working with tabular, spatial, natural language, image, and time-series data.

  • Know English at a B2 level and Polish at a C1 level.

  • Demonstrate core competencies in Analytical Thinking, Learning Agility, Cooperation, and Continuous Improvement & Innovation.

In your daily work, you will handle the following tasks:

  • Designing, developing, and deploying models that solve complex business problems, including predictive, segmentation, forecasting, and recommendation models.

  • Developing query-to-category and query-to-product matching models to enhance the search experience.

  • Building internal AI Engineering competence, including fine-tuning small, efficient models.

  • Taking an active part in all Data Science project phases: from problem formulation and data exploration to modeling, automation, release, and monitoring.

  • Using a wide range of model types, including boosting, Bayesian methods, causal inference, optimization methods, deep learning, and forecasting models.

  • Collaborating across teams, partnering with business stakeholders, analytics, and data engineering teams on experiment setup and deployment.

  • Ensuring models reflect business processes' specificity and staying up-to-date with GenAI challenges.

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