As a Data Scientist, you will be responsible for transforming raw data into actionable insights that drive business decisions. Your primary focus will be on building machine learning models, conducting statistical analysis, and developing predictive models. You will collaborate with cross-functional teams to ensure data is collected, processed, and analyzed efficiently, with a key emphasis on deriving actionable insights, model explainability, and business impact.

Your role

• Develop and implement machine learning models for classification, prediction, and explainability.
• Analyze large datasets to extract meaningful patterns and trends that drive strategic decisions.
• Work closely with data engineers to transform data into analysis-ready formats and manage data pipelines using tools like Spark/PySpark.
• Correlate disparate data sources to uncover actionable business insights.
• Present data-driven findings to stakeholders in clear, understandable terms, ensuring model transparency and explainability.
• Support the transition of machine learning models from development to production, ensuring scalability and performance in real-world applications.
• Produce reports, visualizations, and presentations that outline key business predictions and proposals based on data analysis.

Offer

• Long-term freelance contract
• Solid market rates depending on seniority
• Access to top-notch projects

Requirements

• Data Science & ML Expertise: Extensive experience applying data science techniques for machine learning, including classification tasks and explainability of ML models.
• Programming: Proficiency in Python or Golang for data analysis and model development.
• Big Data Handling: Strong ability to work with large datasets, performing data mining to extract meaningful insights.
• Data Engineering Interaction: Familiarity with data engineering tools and processes, such as Spark or PySpark, for transforming data and managing pipelines.
• Insight Generation: Ability to correlate data and uncover insights that provide business value.
• Communication Skills: Expertise in translating complex data findings into business-friendly reports and presentations, ensuring clarity and actionable insights for stakeholders.
• Model Productionalization: Experience with moving models into production environments and ensuring their robustness.

Nice-to-Have Skills:

• Cross-Functional Collaboration: Experience working closely with software engineers to integrate data-driven features into products and enhance user experience.
• Research & Innovation: Interest in researching new tools, technologies, and methodologies to improve existing data processes and workflows.
• Model Monitoring & Improvement: Experience with monitoring model performance in production environments and recommending optimization strategies.
• Cloud Platforms: Familiarity with cloud environments like Azure, GCP, or AWS for deploying and managing data science projects.