Unlock Business Potential with Data Science

We are seeking a skilled Data Scientist to play a pivotal role in designing and delivering high-quality, data-driven applications that support decision-making and unlock value across our business.

As a Data Scientist at Parameta Solutions, you will work in a collaborative, agile environment with colleagues across data engineering, product, and commercial teams, focusing on scalable solutions and best-in-class coding practices.

This is an exciting opportunity to work with cutting-edge technologies and complex financial datasets, applying statistical methods, data science techniques, and modern software engineering practices to deliver impactful insights and innovative data products.

Our group operates with a start-up mindset, giving you the freedom to explore new tools, adopt best practices, and help shape the future of Parameta Solutions' data strategy.

Our tech stack includes Python, Snowflake, Jupyter, GitLab, Docker, Airflow, AWS, and GCP.

Key Responsibilities

• Build scalable Python applications to query, analyze, and combine large, complex financial datasets using statistical and computational techniques.
• Develop robust quality control and monitoring tools to ensure data accuracy, integrity, and consistency across products and workflows.
• Create insightful dashboards and visualizations to track application performance, monitor key metrics, and communicate results to technical and non-technical stakeholders.
• Support the design, implementation, and deployment of machine learning models and predictive analytics to enhance data-driven decision-making.
• Partner with data engineering, product, and commercial teams to deliver high-quality, scalable applications and solutions aligned with business needs.
• Enhance and maintain existing applications, applying best practices in software development, testing, and documentation.
• Stay current with industry trends in data science, cloud computing, and financial market analytics; proactively recommend new technologies or methodologies to drive efficiency and innovation.
• Contribute to workflow automation using tools such as Airflow and Docker to streamline data pipelines and product delivery.

Essential:

• Advanced degree (Master's or PhD) in Data Science, Mathematics, Engineering, Computer Science, or related field.
• Hands-on professional experience in a numerical or computational role, preferably within financial services or technology.
• Demonstrated proficiency in Python, with emphasis on NumPy, Pandas, SciPy, and best practices in OOP.
• Proficiency in SQL for querying and managing large datasets.
• Experience working with large, complex, and structured/unstructured datasets. Demonstrable problem-solving and analytical skills, with the ability to derive actionable insights from data.
• Exposure to cloud technologies (AWS or GCP).
• Exceptional collaboration and communication skills, with experience working cross-functionally across engineering, product, and commercial teams.

Desired:

• Experience with financial market data, trading workflows, or risk/analytics use cases.
• Familiarity with Snowflake or similar modern data warehouse technologies.
• Experience with Airflow (workflow orchestration) and Docker (containerization).
• Knowledge of CI/CD pipelines (GitLab or similar).
• Hands-on experience with machine learning models (e.g., scikit-learn, TensorFlow, PyTorch).
• Experience developing interactive dashboards (e.g., Plotly Dash, Streamlit, Power BI, Tableau).
• Knowledge of data architecture, reference data, or data pipelines in a financial services context.
• Interest in staying ahead of emerging trends in AI, data science, and financial technology.