Job Description

What do

we expect

from

you?

--------------------------------------------

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.

Your

tasks

------------------

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.

What

you

can

expect

-------------------------------------

Work with leading edge technologies that will enable you to accelerate your career development. Enjoy an excellent work environment where people love what they do. Be part of an international and ambitious team whilst having fun.

Erica Matamoros