Analyst I, Data Science

Liberty Specialty Markets; Colombia; A distancia; COP 66.960.000 - 133.920.000; Jornada completa

Descripción de la vacante

Liberty Specialty Markets is launching a Data Science Analyst hub in Colombia. You will join the Model Automation & Production team to build predictive analytics, work with large datasets, and scale ML pipelines.

The role emphasizes cross-functional collaboration, clear storytelling of findings, and expanding insurance domain expertise. You will contribute to end-to-end model operations, apply ML methods such as GLMs and trees, and deepen your skills in a dynamic, global environment.

Formación

• Fluency in English and Spanish (Must)
• Bachelor’s degree in Mathematics, Statistics, Computer Science, Economics, Actuarial Science, or a related quantitative field; Master’s degree or equivalent industry experience (3+ years) preferred.
• Solid foundation in data extraction, exploratory data analysis (EDA), data transformation, and predictive modelling techniques such as GLM and decision trees.
• Strong analytical and problem-solving skills, with the ability to work with large datasets and derive meaningful insights.
• Effective oral and written communication skills, with the ability to present complex findings clearly to both technical and non-technical audiences.
• Candidates should demonstrate strong business acumen and the ability to quickly learn the insurance domain.
• Collaborative mindset and interpersonal skills to work effectively within cross-functional teams and global environments.
• Interest or experience in insurance, financial services, or fintech, with a desire to deepen domain knowledge.
• Proficiency in Python and SQL. Experience with version control and experiment tracking is desirable but not required.

Responsabilidades

• Apply data science techniques to explore, manipulate, and analyze large structured and unstructured datasets to uncover insights and support business decisions.
• Collaborate on the development and validation of predictive models, including GLMs, decision trees, and other machine learning approaches, aligned with business objectives.
• Formulate and test hypotheses with statistical rigor, ensuring reliability and relevance of analytical outcomes.
• Translate complex quantitative findings into clear visualizations and compelling narratives for diverse audiences, including business stakeholders.
• Contribute to projects of moderate complexity, customizing analytic approaches to meet specific business needs and operational goals.
• Engage with the global Data Science community, participating in cross-functional initiatives and knowledge-sharing forums to promote innovation and best practices.
• Build and apply insurance and financial services domain knowledge to enhance the impact and relevance of your work.

Conocimientos

Python

SQL

English

Spanish

Data analysis

MLOps concepts

GLM

Decision trees

Educación

Bachelor's degree in Mathematics/Statistics/CS/Economics/Actuarial Science

Master’s degree or equivalent industry experience (3+ years) preferred

Herramientas

Pandas

Scikit-learn

Descripción del empleo

The Data Science Solutions team is launching a new global hub in Colombia and seeks talented data science analysts to build a vibrant, high-impact function from the ground up.

As an Analyst in the Model Automation & Production team, you will:

Collaborate with global colleagues to develop predictive analytics that solve real-world business problems.

Work with large structured and unstructured datasets to uncover patterns, build models, and generate actionable insights.

Contribute to automating and scaling machine learning pipelines, model monitoring, and performance improvements.

Communicate findings clearly to technical and non-technical stakeholders to drive data-informed decisions.

Expand your insurance domain knowledge while deepening your expertise in data science and MLOps.

Join a foundational team, gain exposure to end-to-end model operations, and grow your career in a collaborative, innovative-driven environment.

Key Responsibilities:

Apply data science techniques to explore, manipulate, and analyze large structured and unstructured datasets to uncover insights and support business decisions.

Collaborate on the development and validation of predictive models, including GLMs, decision trees, and other machine learning approaches, aligned with business objectives.

Formulate and test hypotheses with statistical rigor, ensuring reliability and relevance of analytical outcomes.

Translate complex quantitative findings into clear visualizations and compelling narratives for diverse audiences, including business stakeholders.

Contribute to projects of moderate complexity, customizing analytic approaches to meet specific business needs and operational goals.

Engage with the global Data Science community, participating in cross-functional initiatives and knowledge-sharing forums to promote innovation and best practices.

Build and apply insurance and financial services domain knowledge to enhance the impact and relevance of your work.

Skills and Experience:

Fluency in English and Spanish (Must)

Bachelor’s degree in Mathematics, Statistics, Computer Science, Economics, Actuarial Science, or a related quantitative field; Master’s degree or equivalent industry experience (3+ years) preferred.

Solid foundation in data extraction, exploratory data analysis (EDA), data transformation, and predictive modelling techniques such as GLM and decision trees.

Strong analytical and problem-solving skills, with the ability to work with large datasets and derive meaningful insights.

Effective oral and written communication skills, with the ability to present complex findings clearly to both technical and non-technical audiences.

Candidates should demonstrate strong business acumen and the ability to quickly learn the insurance domain.

Collaborative mindset and interpersonal skills to work effectively within cross-functional teams and global environments.

Interest or experience in insurance, financial services, or fintech, with a desire to deepen domain knowledge.

Proficiency in Python and SQL. Experience with version control and experiment tracking is desirable but not required.