SENIOR DATA SCIENTIST (MACHINE LEARNING)

Job Description We're searching for a knowledgeable and collaborative Senior Data Scientist to join our global teams.
Based in Lisbon, Portugal this is truly an incredible opportunity to be part of our exciting vision for the future! The Role’s Mission In this pivotal role, the ideal candidate will develop and oversee the development of deep/machine learning models, collaborating with the engineering team to deploy ML algorithms, maintaining production-level code, implementing data engineering for automated solutions, and exploring new ML methods for efficient data analysis.
If you are passionate about pioneering advances in data science, leading strategic initiatives, and contributing to the global impact of our services, apply for a career that’ll be out of the ordinary and join us! Key Outcomes Supervise and inspire data scientists in the team, fostering team building and implementing best practices throughout projects.
Develop and oversee sophisticated deep/machine learning models and statistical algorithms, deploying novel solutions and efficiently retrieving data for robust model building.
Work collaboratively with the Data Engineering team, seamlessly deploying and integrating Machine Learning (ML) algorithms to enhance overall functionality.
Exhibit coding prowess by developing, commenting, maintaining, and sharing legible and production-level programming code, ensuring transparency and reliability.
Explore new ML methods for the streamlined analysis of large datasets.
Create compelling data visualizations, meticulously documenting and communicating findings to internal stakeholders in a structured and impactful manner.
Drive the delivery of effective solutions by consulting with internal stakeholders, solving critical business questions, and providing business recommendations from a Data Science perspective.
Take full responsibility for key projects, consistently raising the performance bar and ensuring the highest standards of excellence.
Identify and harness opportunities for applying data science to improve, optimize, and expand existing solutions.
Key Capabilities Your background will ideally look like this… Academic background (Bachelor’s, Master’s or PhD Degree) in Computer Science, Data Science, Machine Learning Engineering, Statistics or equivalent fields.
Business proficiency in English (B2 Level Minimum Required).
Minimum of 5 years of demonstrable experience in ML Engineering or Data Science roles.
Experience with the end-to-end Data Science cycle.
from data collection to model deployment and monitoring.
Proven experience with Deep Learning and NLP models.
Strong data exploratory skills – understand how to break down the data, extract insights, and determine utility within a broad range of business questions.
Strong knowledge of commonly used ML/Optimization techniques (Classification, Regression, Clustering, Outlier identification) and statistical methodologies (Distributions, Hypothesis testing, Bayesian framework).
Consolidated professional programing experience programming in Python, as well as with Spark and SQL, coupled with adeptness in utilizing prevalent Data Science and Machine Learning libraries.
Experience working within Agile Frameworks (SCRUM/KABAN).
With the following qualities… Inquisitive, with critical thinking, and demonstrate a genuine passion for consumer behaviour.
Enjoys actively looking for new and more efficient ways of improving processes, raising standards, reducing errors, and overcoming omissions.
Is proactive, optimistic, and willing to get involved to achieve the team's goals and objectives.
Is highly collaborative and adaptable with the ability to work effectively within different cultural and technical environments.
Possesses outstanding communication and interpersonal skills in order to comfortably connect with partners at all levels across the organization and facilitates discussions in a constructive manner.
The following, although not mandatory, will be appreciated… Experience with Cloud Computing and storage, ETL, and experience with Big Data handling in Azure Databricks.
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