Mid-level Data Scientist
Description
Company Overview
Lean Tech is a rapidly growing organization headquartered in Medellín, Colombia, with active operations across Latin America and the United States. Focused on software development and IT services, Lean Tech partners with leading companies in the entertainment, financial, and logistics sectors. The company is recognized for its robust engineering teams, collaborative culture, and commitment to professional development. With a strong emphasis on innovation, Lean Tech leverages cutting-edge technologies, including the Microsoft data stack and advanced data engineering and machine learning practices, to deliver impactful solutions. Lean Tech’s inclusive environment offers significant opportunities for career advancement, making it an ideal workplace for professionals seeking technical excellence and growth within a dynamic, international setting.
Position Overview
This role is responsible for leveraging advanced data science and machine learning techniques to address complex business challenges within a Microsoft-centric environment. Core responsibilities include designing, building, and refining predictive models for applications such as demand forecasting, customer transcript analysis, marketing ROI optimization, and product return prediction. You will utilize tools like Azure Machine Learning, AutoML, Azure Data Factory, Azure Fabric, SQL Server, and Power BI, while applying strong proficiency in Python and SQL for model development, data processing, and MLOps tasks.
As a key member of the analytics and AI team, you will collaborate closely with data engineers to structure and organize data using the medallion architecture (bronze, silver, gold layers) for maximum data usability and quality. Additional responsibilities include performing feature engineering, statistical analysis, and developing BI dashboards using tools such as Tableau and Looker. You will ensure high standards for code quality, modularity, automated testing, infrastructure as code, model monitoring, and CI/CD pipelines to deliver production-grade machine learning solutions.
This position plays a vital role in providing actionable insights and supporting business decision-making, engaging with cross-functional teams to translate data into strategies that drive organizational success. The role offers the unique challenge of navigating and optimizing within a rapidly evolving Azure data ecosystem while ensuring integration with enterprise reporting and analytics. Your expertise will directly impact Lean Tech’s operations and continued growth across multiple industries.
What You Will Be Doing
- Design, build, and optimize advanced machine learning models to solve business problems such as demand forecasting, customer call center transcript analysis, marketing ROI optimization, and product return prediction.
- Develop and maintain data processing and ETL pipelines using Python and SQL for data manipulation, automation, and integration with machine learning workflows.
- Leverage Microsoft data and AI technologies, including Azure Fabric, Azure Machine Learning, AutoML, Azure Data Lake, SQL Server, NoSQL, and Copilot Studio, to deliver scalable analytics and AI solutions.
- Utilize Azure Data Factory for orchestration of data pipelines and automated model training processes, ensuring efficient flow and accessibility of data.
- Partner with data engineers to prepare and structure data following the medallion architecture (bronze, silver, gold layers), enhancing data quality and usability for analytics and machine learning initiatives.
- Perform comprehensive data preprocessing, feature engineering, and statistical analysis to ensure high-quality input for modeling and analytics tasks.
- Evaluate, test, and refine machine learning models for accuracy, scalability, and measurable business impact, employing automated testing, CI/CD pipelines, and model monitoring for production readiness.
- Integrate and visualize model outputs and business insights using BI tools such as Power BI, Tableau, and Looker, as well as apply knowledge of data warehousing concepts for effective reporting.
- Write advanced analytic queries and optimize SQL performance on large-scale data platforms to support efficient data analysis and reporting.
- Collaborate closely with cross-functional teams—including engineering, BI analysts, and business leaders—to translate data-driven insights into actionable business strategies.
- Adhere to software engineering best practices, including Infrastructure as Code, separation of environments, modularity, code quality, and maintainability in developing production-grade solutions.
- Stay up to date with advancements in AI, machine learning, and the Microsoft ecosystem to continuously improve and innovate approaches for addressing evolving business needs.
Required Skills & Experience
- Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or a related field
- 3+ years of experience in data science or applied machine learning
- Advanced proficiency in Python for building machine learning pipelines, data processing, ETL tasks, and MLOps activities such as model packaging and deployment
- Advanced proficiency in SQL, including writing advanced analytic queries and optimizing query performance on large-scale data platforms
- Proven ability to develop predictive models and deliver insights that impact business outcomes
- Experience applying advanced machine learning techniques to solve business challenges
- Experience with the Microsoft data stack, including Azure Fabric, Azure Machine Learning, AutoML, SQL Server, NoSQL, and Azure Data Lake
- Experience designing and managing Azure-based ML pipelines, including leveraging AutoML for automated model selection and training
- Experience with data pipeline orchestration tools, specifically Azure Data Factory
- Familiarity with medallion architecture (bronze/silver/gold layers) and modern data engineering practices
- Experience collaborating with data engineers to structure data using the medallion architecture
- Intermediate knowledge of data warehousing concepts
- Experience with BI tools such as Power BI, Tableau, and Looker for dashboarding, reporting, and integrating ML outputs into BI workflows
- Strong statistical analysis and feature engineering skills
- Knowledge of software engineering best practices for ML model production: infrastructure as code, separation of environments, modularity, automated testing, CI/CD pipelines, and model monitoring
- Excellent communication skills for effectively conveying technical findings to diverse stakeholders
Nice to Have
- Experience with additional cloud platforms such as AWS or Google Cloud Platform
- Familiarity with data visualization libraries in Python (e.g., Matplotlib, Seaborn, Plotly)
- Exposure to data governance or data security best practices
- Professional certifications in Azure, data science, or machine learning
- Experience with agile or other collaborative project management methodologies
- Background in industries relevant to entertainment, finance, or logistics
- Familiarity with containerization tools such as Docker or Kubernetes
- Ability to mentor or provide guidance to junior team members
- Strong problem-solving aptitude and adaptability in dynamic environments
- Demonstrated willingness to learn emerging technologies and methodologies
Soft Skills
- Clear and effective communication skills, essential for conveying complex technical findings to both technical and non-technical stakeholders and for collaborating with cross-functional teams.
- Strong problem-solving abilities, demonstrated by designing and refining machine learning models to address diverse business challenges in areas such as demand forecasting, marketing ROI optimization, and product return prediction.
- Collaboration and teamwork, reflected in close partnerships with data engineers to implement medallion architecture and with business intelligence analysts and business leaders to translate data insights into strategic actions.
- Proactive adaptability and willingness to stay current with advancements in AI, machine learning, and the Microsoft data and AI ecosystem, ensuring innovative and up-to-date solutions.
- Attention to detail, applied in data preprocessing, feature engineering, statistical analysis, and optimization of SQL queries for large-scale data platforms to ensure data and model accuracy.
- Project ownership and accountability, evidenced by continuous evaluation, testing, and refinement of data science solutions to maximize business impact.
- Commitment to best practices in software engineering, including modularity, code quality, automated testing, CI/CD pipelines, and model monitoring, to deliver maintainable and production-grade solutions.
Why You Will Love Working with Lean Tech
Join a powerful tech workforce and help us change the world through technology
- Professional development opportunities with international customers
- Collaborative work environment
- Career path and mentorship programs that will lead to new levels.
Join Lean Tech and contribute to shaping the data landscape within a dynamic and growing organization. Your skills will be honed, and your contributions will play a vital role in our continued success. Lean Tech is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.