Data Scientist (Information Retrieval)

OUR WORK MODEL

Remote or Hybrid: This can be a remote or hybrid role depending on your location and proximity to one of our local offices.

YOUR ROLE

As a Data Scientist, you will design and implement advanced models, drive innovation through machine learning and AI, and mentor team members. In this high-impact role, your work will directly shape Cognism’s data-driven products and business strategies.

YOUR CHALLENGES & OPPORTUNITIES

• Develop Advanced Models – Build and maintain sophisticated statistical and machine learning models that drive business decisions and integrate with enterprise systems.
• Lead Innovation – Design and pilot cutting-edge predictive models and algorithms, testing scalability and implementing successful approaches into production environments.
• Own Projects End-to-End – Scope, design, implement, and evaluate data science solutions independently, while keeping stakeholders aligned on progress and results.
• Drive Business Value – Collaborate with cross-functional teams to identify opportunities where data science can make a measurable impact on customer experience or operational efficiency.
• Mentor Team Members – Support and guide junior data scientists by sharing best practices, providing feedback, and contributing to an inclusive, knowledge-sharing culture.
• Shape Data Strategy – Work closely with the Data Science Manager to prioritize projects and align outputs with strategic goals.

OUR EXPECTATIONS

• Professional Experience – 3+ years in a data science role, with a strong track record of delivering impactful, production-ready solutions.
• Technical Expertise – Proficient in Python, Spark, and machine learning frameworks; strong understanding of statistical modeling and big data processing.
• Cloud Proficiency – Experience with AWS tools such as S3, SageMaker Studio, Glue Jobs, and Athena is highly desirable.
• Educational Background – PhD preferred in Data Science, NLP, Information Retrieval, Mathematics, or Statistics. A Master’s degree with significant experience will also be considered.
• Problem-Solving Ability – Strong analytical mindset with the ability to design practical, scalable solutions to complex problems.
• Communication Skills – Comfortable explaining technical concepts to non-technical stakeholders and influencing decisions through data insights.
• Collaborative Approach – Proven ability to work effectively in cross-functional teams and agile environments.

OUR TECH STACK

• Python & PySpark – Build, test, and deploy machine learning pipelines using scalable data processing tools.
• AWS Ecosystem – Leverage services like SageMaker, S3, Glue, and Athena for data engineering and ML model deployment.
• ML Frameworks – Work with tools such as scikit-learn, TensorFlow, or similar libraries to experiment and optimize models.
• Version Control – Use Git and CI/CD tools to manage code and streamline development workflows.
• Data Visualization – Communicate findings with clarity using tools like matplotlib, seaborn, or business-friendly dashboards.
• SQL & Big Data – Write efficient queries to extract insights from large, complex data sets.