IC3 - Data Scientist

Objective of the Role 

Support the Decision Science team by analyzing complex data, developing and optimizing machine learning models, and effectively communicating results to drive data-driven decision-making and innovation within the organization.  

Responsibilities typically include conducting data collection and preprocessing, exploratory data analysis, statistical analysis, feature engineering, model evaluation and validation, model deployment and monitoring tasks. They are expected to have a deeper understanding of machine learning algorithms, statistical techniques, and domain knowledge relevant to the organization. Mid Data Scientists work under the direct supervision of the Lead Data Scientist and the indirect guidance of an assigned Senior Data Scientist, gaining hands-on experience in data science methodologies, tools, and techniques. 

They contribute to projects aimed at improving organizational efficiency, identifying growth opportunities, and minimizing risks using machine learning.  

Overall, the goal is to apply advanced data science expertise to generate valuable insights, improve decision-making processes, and drive innovation within the organization. By doing so, they will develop foundational skills and knowledge in data science, ensuring their growth and effectiveness as valuable team members. 

Main Responsibilities 

• Gather, cleanse, and preprocess large and complex datasets from various internal and external sources, ensuring data quality and integrity. 

• Conduct in-depth exploratory data analysis to understand data distributions, identify patterns, and uncover insights using advanced statistical methods and data visualization tools. 

• Perform advanced statistical analyses to summarize and interpret data, including hypothesis testing, regression analysis, and other inferential techniques. 

• Create, select, and transform features from raw data to improve model performance, leveraging domain knowledge and advanced feature engineering techniques. 

• Develop and optimize machine learning models for predictive analytics, classification, regression, clustering, and other purposes. Experiment with various algorithms and techniques to identify the best solutions for specific problems. 

• Rigorously evaluate models using techniques such as cross-validation and various performance metrics to ensure models are robust, accurate, and generalizable. 

• Deploy models into production environments, monitor their performance, and make necessary adjustments to maintain and improve their effectiveness over time. 

• 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. 

• Work collaboratively with other data scientists within the team, as well as participate in cross-functional teams that may include data engineers, data analysts, business analysts, and domain experts. Aim to create data products and integrate them into the organization's business processes. 

• Stay updated on emerging trends, methodologies, and tools in data science, machine learning, and artificial intelligence. Participate in continuous learning and apply new knowledge to improve analytical capabilities and drive innovation within the organization. 

• Promote an autonomous work culture by encouraging self-management, accountability, and proactive problem-solving among team members. 

• Serve as a Spin Culture Ambassador to foster and maintain a positive, inclusive, and dynamic work environment that aligns with the company's values and culture. 

Required Knowledge and Experience 

• Minimum 3-5 years in data science, statistics, programming, or related fields, with hands-on experience in data analysis, machine learning, and statistical modeling. 

• Bachelor’s or Master’s degree in Data Science, Statistics, Applied Mathematics, Actuarial Science, Computer Science, or a related discipline. Desirable: Advanced coursework or specialized training in machine learning, statistics, or data science. 

• Proficiency in programming languages commonly used in data science, such as Python or R. Familiarity with libraries and frameworks like NumPy, Pandas, scikit-learn (Python), or tidyverse (R) for data manipulation, analysis, and modeling. 

• Strong understanding of statistical concepts and techniques, including hypothesis testing, regression analysis, and probability distributions. 

• In-depth knowledge of machine learning algorithms and techniques, including supervised and unsupervised learning, ensemble methods, and deep learning, including linear regression, logistic regression, decision trees, random forests, k-nearest neighbors (KNN), and clustering algorithms. Familiarity with model evaluation metrics and techniques. 

• Experience in data preprocessing and cleaning techniques, including handling missing values, outliers, and data imputation. Ability to manipulate and transform data to prepare it for analysis and modeling. 

• Proficiency in SQL for querying, manipulating, and retrieving data from relational databases. 

• Ability to create visualizations and dashboards to communicate insights and findings effectively. Familiarity with data visualization tools and libraries such as Matplotlib, Seaborn, ggplot2, or Tableau. 

• Experience with version control systems like Git for managing code repositories, tracking changes, and collaborating with team members. 

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

• Basic understanding of the specific industry or domain in which the organization operates. Awareness of industry-specific trends, regulations, and business processes. 

• Strong analytical and problem-solving skills, with the ability to break down complex problems, formulate hypotheses, and develop data-driven solutions. 

• Excellent communication skills to articulate findings, insights, and recommendations to both technical and non-technical stakeholders in a clear and understandable manner. 

• Understand ethical considerations and best practices in data science, including data privacy, confidentiality, and bias mitigation. 

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. 
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