IC4 – Sr Data Scientist

Objective of the Role:

As a Senior Data Scientist, you will lead the design, development, and optimization of advanced machine learning models to solve complex business challenges and drive measurable impact. You will be part of the Analytics Chapter, where you’ll help set technical standards, share knowledge, and mentor other Data Scientists, while your daily work will be embedded within a cross-functional squad, collaborating closely with Product, Business, Engineering, and MLOps teams. This role is ideal for professionals with deep expertise in algorithm selection, experimentation, and model performance optimization, who are passionate about elevating data science practices and shaping high-quality solutions. Supported by the broader Analytics Chapter community, you will contribute to continuous learning, foster technical excellence, and help build a culture of data-driven innovation across squads.

Main Responsibilities

● Lead end-to-end data science projects, encompassing ideation, objective definition, scope, and deliverables, in collaboration with stakeholders, through to deployment within an ML Lifecycle framework.

● Conduct in-depth exploratory data analysis to understand data distributions, identify patterns, and uncover insights using advanced statistical methods and data visualization tools, leveraging domain knowledge to contextualize findings, identify relevant variables, and tailor solutions to meet business needs.

● Design, develop, and optimize advanced machine learning models for a wide range of applications (e.g., predictive analytics, classification, forecasting), experimenting with advanced algorithms, feature engineering, and selection leveraging domain knowledge, and rigorously evaluating model performance with an ML Lifecycle perspective.

● Gather, cleanse, and preprocess large and complex datasets from various internal and external sources, ensuring data consistency and integrity.Collaborate with MLOps engineers to deploy, monitor, and maintain the models in production environments to ensure ongoing effectiveness and accuracy in a ML Lifecycle perspective.

● Mentor junior and mid-level data scientists, fostering continuous learning and development through best practices, feedback, and professional growth support.

● Collaborate with data scientists, engineers, analysts, and domain experts to create and integrate data products into business processes.

● Prepare detailed documentation of data analysis processes, methodologies, and results. Create comprehensive reports and presentations to communicate findings and recommendations to technical and non-technical stakeholders.

● Stay updated with data science and machine learning trends and best practices. Continuously learn and apply new knowledge to enhance analytical capabilities and foster innovation within the organization.

● Contribute to a culture of autonomy, accountability, and proactive problem-solving.

● Act as a Spin Culture Ambassador, promoting a positive and inclusive work environment aligned with company values.

Required Knowledge and Experience

● Minimum 4–6 years in data science, statistics, programming, or related fields, with extensive experience in data analysis, machine learning, and statistical modeling.

● Bachelor’s or Master’s in Data Science, Statistics, Applied Math, Actuarial Science, or Computer Science. Advanced training in ML,statistics, or data science is desirable.

● Experience leading and executing data science projects in diverse domains and working on a wide range of projects, from exploratory analysis to building advanced machine learning models and predictive analytics solutions.

● Proficient in data science programming languages (Python) and libraries (NumPy, Pandas, scikit-learn,) for data manipulation, analysis, and modeling.

● Experience in advanced statistical techniques, including multivariate analysis, time series analysis, hypothesis testing, and experimental design. Ability to apply statistical methods to analyze complex datasets and derive meaningful insights.

● Extensive experience in developing, fine-tuning, and deploying machine learning models and predictive analytics solutions.

● Deep understanding of a wide range of machine learning algorithms, including supervised learning (linear regression, logistic regression, decision trees, random forests), unsupervised learning (clustering),ensemble methods (gradient boosting, bagging), time series and recommendation systems. Ability to select and apply appropriate algorithms to solve specific business problems.

● Proficient in SQL for data manipulation and analysis. Familiar with NoSQL, big data technologies (Spark,PySpark), and distributed computing.

● Strong analytical and problem-solving skills, with the ability to critically evaluate data and models, identify biases, and make data-driven decisions.

● Highly skilled in data visualization and storytelling, using tools like Matplotlib, Seaborn, Plotly, to communicate complex findings to non-technical stakeholders through clear, compelling visuals.

● Desire: Substantial understanding of the industry in which the organization operates. Knowledge of industry-specific trends, regulations, and business processes. Ability to apply domain knowledge to contextualize data analysis findings and drive strategic decision-making

Spin está comprometida con un lugar de trabajo diverso e inclusivo. 
Somos un empleador que ofrece igualdad de oportunidades y no discrimina por motivos de raza, origen nacional, género, identidad de género, orientación sexual, discapacidad, edad u otra condición legalmente protegida. 
Si desea solicitar una adaptación, notifique a su Reclutador.