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BearingPoint

Data Scientist, Senior Data Analyst – Data Analytics, AI

BearingPoint

Data Scientist & Senior Data Analyst in Data Analytics & AI at BearingPoint, delivering advanced analytics initiatives and AI-driven solutions for diverse clients. Collaborating with cross-functional teams to translate data into actionable insights.

Posted 5/13/2026full-timePrague • CzechiaSeniorWebsite

Tech Stack

Tools & technologies
KerasNoSQLPandasPythonPyTorchScikit-LearnSparkSQLTableauTensorflow

About the role

Key responsibilities & impact
  • Design, develop, and implement advanced analytics and AI use cases end-to-end, tailored to client needs.
  • Perform in-depth data analyses (descriptive, diagnostic, predictive) on large datasets and support clients with clear, impactful insights and recommendations.
  • Build, train, and validate machine learning models and AI solutions using state-of-the-art tools and frameworks.
  • Interpret and visualize analytical results. Present complex findings clearly to both technical and non-technical client stakeholders.
  • Work closely with clients to understand their business challenges and data landscape. Advise on analytics strategy, data management best practices, and opportunities to leverage emerging technologies.
  • Coordinate project tasks among team members, ensuring timely delivery of high-quality results and mentoring junior analysts.

Requirements

What you’ll need
  • Master’s degree (or equivalent) in Data Science, Computer Science, Mathematics, Engineering, Statistics, or a related field (A Bachelor’s degree combined with strong relevant experience will also be considered).
  • Proven ability to extract, prepare, and analyze data from diverse sources (structured and unstructured).
  • Strong proficiency in SQL and data querying; familiarity with data modeling and database concepts (NoSQL knowledge is a plus).
  • Proficiency in analytics programming, especially Python (and/or R). Hands-on experience with data science libraries and frameworks (e.g., pandas, scikit-learn, TensorFlow, Keras, PyTorch, Spark MLlib) and data visualization tools (e.g., Power BI, Tableau).
  • Solid foundation in statistics and mathematics to develop, validate, and interpret complex analytical models.
  • Experience building machine learning models (supervised and unsupervised) and knowledge of the end-to-end model lifecycle from development to deployment.
  • Excellent communication and presentation skills. Ability to convey technical concepts and insights in a clear, compelling way to business stakeholders.
  • Experience in a client-facing role or consulting environment, demonstrating a client-focused approach and the ability to adapt solutions to different business contexts.
  • Several years (typically 3–5+ years) of hands-on experience in data analytics, data science, or a related field.
  • EU citizenship or Czech Permanent Residency and already living in the Czech Republic.

Benefits

Comp & perks
  • Attractive compensation package: fixed salary with yearly evaluation and performance-related bonus, seniority, referral bonus, and peer-to-peer recognition.
  • Multisport card
  • Private medical subscription at Canadian Medical
  • Prague transportation benefit (Lítačka)
  • Pension insurance contribution
  • Sick days
  • Hybrid working model
  • Seniority vacation days
  • Continuous Learning & Growth: learning-oriented culture with customized training paths, support for professional certifications.
  • Inclusive Team Culture: collaborative, inclusive environment that values diversity, knowledge-sharing, and entrepreneurial spirit.

ATS Keywords

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Hard Skills & Tools
data analysismachine learningdata preparationdata modelingstatisticsanalytics programmingdata visualizationSQLPythonR
Soft Skills
communicationpresentationclient-focusedmentoringadaptabilitycollaborationproblem-solvinganalytical thinkingstrategic advisingproject coordination
Certifications
Master’s degree in Data ScienceMaster’s degree in Computer ScienceMaster’s degree in MathematicsMaster’s degree in EngineeringMaster’s degree in Statistics