Senior Data Scientist

OpportuLand; Polska; Hybrid; PLN 210,000 - 260,000; Full time

Job summary

OpportuLand is seeking a Senior Data Scientist to design and implement advanced data models and ML algorithms to optimize commercial performance in the publishing domain. You will analyze cross-functional data (sales, marketing, pricing) and build scalable data pipelines using Snowflake, DBT, and modern ML frameworks while collaborating with business and technical stakeholders.

Excellent English communication is required.

Qualifications

• Bachelor’s or Master’s degree in Data Science, Computer Science, Engineering, Statistics, Mathematics, or related field.
• 5+ years of experience in data science roles.
• Proficient in Python and SQL for data analysis, scripting, and machine learning.
• Experience with modern data warehouses/lakehouses (Snowflake, Databricks, BigQuery, Redshift).
• Experience with SciKit-learn, Pytorch and similar frameworks for building ML models.

Responsibilities

• Design, develop, and implement advanced data models and ML algorithms for business problems in publishing.
• Analyze sales, marketing, pricing, and customer data to identify opportunities and support decisions.
• Build scalable data pipelines using Snowflake, DBT, and related tools.
• Collaborate with stakeholders to translate business challenges into analytical solutions.
• Stay current with trends in data science and ML.

Skills

Python

SQL

Machine learning

Data analysis

Snowflake

Databricks

BigQuery

Pytorch

Scikit-learn

DBT

English

Education

Bachelor/Master in DS/CS

Tools

Snowflake

Databricks

BigQuery

Redshift

DBT

SciKit-learn

Pytorch

Job description

## Senior Data ScientistWe are looking for a highly skilled Senior Data Scientist to join our team. As a Senior Data Scientist, you will be responsible for designing and implementing advanced data models, analyzing complex business problems, and developing solutions that help optimize sales, marketing, pricing, and overall commercial performance in a leading American publishing house.The role will contribute to the transition from primarily descriptive analytics toward more advanced, data-driven decision making.Skills & ExperienceBachelor’s or Master’s degree in Data Science, Computer Science, Engineering, Statistics, Mathematics, or a related field;5+ years of experience in data science roles;Strong experience applying data science and machine learning to business problems, ideally in areas such as sales, marketing, customer behavior, demand forecasting, or pricing optimization;Proficient in Python and SQL for data analysis, scripting, and machine learning;Proficient in using agentic coding tools like Claude Code, Codex, CursorExperience with SciKit-learn, Pytorch, and similar machine learning frameworks for building and deploying machine learning models;Experience working with modern data warehouses/lakehouses like Snowflake, Databricks, BigQuery, RedshiftStrong analytical and problem-solving skills, with the ability to translate ambiguous business problems into data-driven solutions;Ability to work independently and collaborate with business and technical stakeholders;Fluent in English and excellent communication skills.Nice to have:Strong background in data science, with extensive experience in leveraging advanced analytics and machine learning to drive data-driven business decisions.ResponsibilitiesDesign, develop, and implement advanced data models and machine learning algorithms to solve complex problems in the publishing domain, like author discovery;Analyze sales, marketing, customer, product, and pricing data to identify opportunities and support business decisions;Develop data-driven approaches to areas such as demand forecasting, customer behavior, sales optimization, marketing effectiveness, and pricing;Work with business stakeholders to understand broad or ambiguous business challenges and translate them into analytical and machine learning solutions;Collaborate with cross-functional teams to understand data requirements and deliver actionable insights and recommendations;Build and maintain scalable data pipelines using technologies such as Snowflake, and DBT;Leverage Python and SQL for data analysis, model development, experimentation, and automation;Utilize SciKit-learn, Pytorch and similar frameworks for developing and deploying machine learning models;Help evolve the organization from descriptive analytics toward predictive and prescriptive analytics and data-driven decision making;Stay current with emerging trends and technologies in data science and machine learning.