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Data Scientist (all genders)

Flaconi Sourced

Berlin, Berlin, Germany Full-time Not specified

No account needed — this role is applied for on the employer’s own site.

About the role

<p>At flaconi, we believe that&nbsp;<span class="lila-hg"><strong>diversity</strong></span>&nbsp;fuels creativity, innovation and growth. It thrives in all its forms of beauty. Our team is a mosaic of individuals from different backgrounds, cultures and life journeys, all united by a shared passion for beauty. Our commitment is grounded in our core values, particularly&nbsp;<span class="lila-hg"><strong>#trustinpeople</strong></span>. We hold ourselves accountable for creating an environment where every employee feels honestly valued, heard and empowered to contribute their unique perspectives to foster belonging.</p> <p>flaconi is an&nbsp;<span class="lila-hg"><strong>equal opportunity employer</strong></span> and welcomes and encourages all qualified&nbsp; from all backgrounds and identities, regardless of ethnicity, race, gender identity, migration background, national origin, disability status, sexual orientation, religion, or age to apply.&nbsp;</p> <p>If you require specific support during the application or interview process – e.g. live captions, more time for the interview or the task in a different format – just let us know so that we can adapt the process accordingly. We will do our best to make this possible.<br><br>We are seeking a (Senior) Data Scientist (all genders) to join a dynamic team of five within our DATA department. You'll dive into diverse challenges including training and deploying machine learning models, developing Generative AI and Agentic AI solutions, and statistical modeling. We believe in flat hierarchies, which means you'll work closely with colleagues of various seniorities in a fun environment. You'll play a key role in extracting valuable insights and developing data and AI solutions to optimize our e-commerce platform across Europe, taking end-to-end ownership of your projects and collaborating closely with business stakeholders along the way. Your contributions will have a direct impact on business decisions and process efficiency across a wide spectrum of unique use cases.</p> <p><strong>Your Role<br></strong></p> <ul> <li data-path-to-node="9,0,0">Employ statistical techniques and machine learning algorithms for regression, classification, and clustering (e.g., Gradient Boosting, Random Forest, Hierarchical Clustering, ensemble methods).</li> <li data-path-to-node="9,1,0">Develop and productionalize Generative AI and Agentic AI solutions alongside modern NLP techniques using transformer architectures (e.g., BERT, GPT).</li> <li data-path-to-node="9,2,0">Utilize time-series forecasting methods (e.g., ARIMA, LSTM, DeepAR, Prophet) for predictive modeling.</li> <li data-path-to-node="9,3,0">Conduct exploratory data analysis (EDA), advanced feature engineering, and dimensionality reduction (PCA, t-SNE).</li> <li data-path-to-node="9,4,0">Develop and implement deep learning models in PyTorch, TensorFlow, or Keras while leveraging transfer learning strategies.</li> <li data-path-to-node="9,5,0">Showcase your skills across key e-commerce data science use cases including demand forecasting, churn and CLTV modeling, pricing, customer segmentation, recommender systems, and personalization.</li> <li data-path-to-node="9,6,0">Handle large-scale datasets using distributed computing frameworks (Apache Spark) on a tech stack comprising Python (PySpark, pandas, NumPy, scikit-learn, LightGBM), SQL, Databricks (Data Lakehouse &amp; ML platform), AWS (S3, Lambda, EC2, SageMaker, Bedrock), GitHub, and AI-augmented tools (Claude, Copilot) while collaborating directly to proactively address stakeholder business needs.</li> </ul> <p><strong>Your Profile<br></strong></p> <ul> <li data-path-to-node="12,1,0">Bring a minimum of 3 years of hands-on experience in developing and deploying machine learning solutions for real-world business problems.</li> <li data-path-to-node="12,2,0">Demonstrate background exposure to <strong data-path-to-node="12,2,0" data-index-in-node="35">e-commerce (ideal)</strong>, <strong data

Skills

Data Science

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