Scientist

at  CLEARPEAKS

ClearPeaks is a specialist consulting firm delivering services and solutions in “Everything Data” – Business Intelligence, Advanced Analytics, Big Data & Cloud, and Web & Mobile Applications. Founded in 2000, we have been a trusted partner to our customers in over 15 industry verticals and functional areas, with operations spanning Europe, Middle East, the United States, and Africa.
ClearPeaks is part of a strategic alliance with synvert, a group of six successful full-service Data & Analytics (D&A) consulting firms, with a clear goal to become one of EMEA’s largest D&A consulting companies.
Our services are based on the latest market-leading enterprise technology platforms, and delivered by a dynamic team of expert consultants. Our strength lies in our ability to efficiently deliver customer insight and value, gained through our decades of experience with real-world challenges.
As a Senior Data Scientist, you will will engage deeply in data exploration, model development, and the implementation of advanced analytics and machine learning solutions. This role is centered on hands-on data manipulation, building predictive models, and generating actionable insights to drive data-informed decisions across the organization.
At ClearPeaks, there are endless opportunities to get involved in different projects bringing innovative ideas while being part of leading-edge teams who are always innovating and evolving in the Data Management field.

REQUIREMENTS

• Bachelor’s degree in computer science, Data Analytics, Mathematics, Information Management, or Statistics. A master’s degree is preferred with a focus on Data Science, Machine Learning or a related discipline.
• Minimum of 8 years in data science, analytics, or a related technical field, with hands-on experience in machine learning and data modeling.
• Demonstrated success in developing and deploying data science projects in large organizations or complex environments.
• Programming Languages: Proficiency in Python, R, or similar languages for data analysis and model development.
• Data Modeling: Strong skills in data modeling with experience in SQL and NoSQL databases.
• Machine Learning Frameworks: Expertise in frameworks such as TensorFlow, PyTorch, Scikit-learn, or similar.
• Data Science Platforms: Experience with Dataiku Data Science platform and familiarity with Microsoft Azure services, including Azure ML Studio and Azure Copilot Studio.
• Data Visualization: Proficiency in data visualization tools like Power BI, Tableau, or similar platforms
• Massive Parallel Processing (MPP) Technologies: Familiarity with MPP platforms (e.g., Databricks, Snowflake) is a plus.
• Version Control: Experience with Microsoft DevOps as version control systems.
• Data Science Certification from Microsoft and Dataiku (preferred). Additional certifications in machine learning or data analytics are a plus.

• Data Analysis: Perform comprehensive analysis of structured and unstructured datasets using statistical and exploratory data analysis (EDA) techniques to uncover patterns and insights.
• Data Cleaning and Transformation: Clean, preprocess, and transform raw data to ensure quality and readiness for analysis and modeling.
• Feature Engineering: Develop and select relevant features to enhance model performance and predictive capabilities.
• Machine Learning: Design, implement, and validate machine learning and deep learning models using frameworks such as TensorFlow, PyTorch, or similar.
• Predictive Modeling: Create and refine predictive models to solve business problems, leveraging techniques like regression, classification, clustering, and time-series analysis.
• Scalable Pipelines: Develop and maintain scalable data pipelines using SQL, NoSQL, and other relevant technologies to handle large datasets efficiently.
• Model Deployment: Deploy models into production environments using platforms like Dataiku, ensuring seamless integration and performance monitoring.
• Statistical Analysis: Apply advanced statistical methods to interpret complex data sets and derive meaningful insights.
• Data Visualization: Create compelling visualizations and dashboards using Power BI to effectively communicate findings to technical and non-technical stakeholders.
• Data Storytelling: Translate analytical results into clear, actionable narratives that support strategic decision-making.
• Cross-Functional Collaboration: Work closely with product owners and other data professionals to develop analytical data products.
• Technical Support: Provide support to developed products.
• Technical Documentation: Maintain thorough documentation of data processes, models, and methodologies to ensure reproducibility and knowledge sharing.