JOB SUMMARY
The data scientist (DS) is a key enabler for the achievement of Opella ’s objectives of leveraging data, digital, and ML-based solutions to drastically improve marketing, and financial operations. Discovering and implementing solutions using data is critical to the success of this role.
The DS plays a key role in the deployment, and continuous improvement of machine learning pipelines generating insights for business. He/she is required to leverage large volumes of heterogenous data, evaluate the performance of existing models, and develop new machine learning pipelines in order to
enhance the business’s understanding of individual population segments, investment outcomes, anticipate changes in our customer patterns, etc.
The DS will be involved in projects of different nature such as time series forecasting, data segmentation, natural language processing, constraint programming and optimization across the whole Opella organization.
MAIN DUTIES AND RESPONSIBILITIES
Key responsibilities include but are not limited to:
Work under the supervision of his tutor within the Advanced Analytics team- Work closely with product owners, developers, engineers, and MLOps to deliver AI/ML solutions
- Implementation of new machine learning pipelines following the guidelines of senior DS scientists
- Identify potential improvements on existing models
- Exploratory analysis and integration of new datasets
- Design and implementation of POCs to validate or determine the adoption of state-of-the-art machine learning algorithms
- Deployment, running, monitoring and postmortem analysis of machine learning models with a special focus on time series analysis
- Drive continuous improvement of existing time series forecasting projects
- Deliver actionable business insights through advanced statistical analysis, ML, and data visualization
- Support the harmonization and standardization of ML solutions
What we offer:
Excellent opportunity to learn and work with scalable ML technologies (AWS, Python, ML libraries, MLFlow, AirFlow, CI/CD, etc.)- Exposure to all stages of data science projects, guided by the OSEMIN life cycle model
- An international working environment where you can develop your talent within a competent and innovative team
- Involvement in the definition, design, deployment, and support of cutting-edge solutions within a global pharmaceutical company
- A professional setting offering numerous opportunities for personal and career development
- Access to internal training programs supported by Opella’s Talent Management team
REQUIREMENTS
EDUCATIONAL BACKGROUND
- Access to internal training programs supported by Opella’s Talent Management team
PROFESSIONAL EXPERIENCE
We value candidates with:
- Experience in Object-Oriented Programming using Python
- Hands-on experience in AI/ML modeling of complex datasets, with strong theoretical knowledge
- Expertise in one or more of the following areas: supervised learning, unsupervised learning, deep learning, time series forecasting, Bayesian statistics, and optimization
- International exposure (e.g., academic programs, international courses)
- Experience in interdisciplinary domains involving scientific, industrial, marketing, or commercial stakeholders
- Excellent communication skills, including business analysis, data visualization, and storytelling
- Familiarity with cloud and high-performance computing platforms (e.g., AWS, GCP, Databricks, Apache Spark)
- Knowledge of probability theory and mathematical statistics (a plus)
MAJOR SKILLS AND COMPETENCIES
- Proficient in Python with a solid understanding of OOP concepts
- Experience working with Snowflake and other database systems
- Experience in cloud environments such as AWS, GCP, or Azure
- Strong English skills both written and spoken
- Analytical mindset with a flexible, proactive, and results-oriented attitude
- High productivity and effectiveness in remote work settings
- Learning mindset and strong self-motivation
- Ability to manage multiple tasks in a fast-paced environment
- Ability to thrive in a multicultural and international team, promoting transparency, constructive feedback, mutual respect, and integrity