Senior Data Scientist

Job Description

Data Science

Business Intelligence Engineer

Development

Data Engineering

Responsibilities

Job Responsibilities:

Data Engineering

• Design and manage high-throughput, low-latency data pipelines using distributed computing frameworks.
• Build scalable ETL/ELT workflows using tools like Airflow and Spark.
• Work with containerised environments (e.g., Kubernetes, OpenShift) and real-time data platforms (e.g., Apache Kafka, Flink).
• Ensure efficient data ingestion, transformation, and integration from multiple sources.
• Maintain data integrity, reliability, and governance across systems.

Data Analysis And Modelling

• Apply statistical and machine learning techniques to analyse data and translate complex data sets to identify patterns, trends and actionable insights that drive business strategy and operational efficiency.
• Develop predictive models, recommendation systems, and optimisation algorithms to solve business challenges and enhance operational efficiency.
• Transform raw data into meaningful features that improve model performance and translate business challenges into analytical problems providing data driven solutions.

Machine Learning And AI Development

• Build and implement advanced statistical and machine learning models to solve complex problems.
• Identify data quality issues and work with data engineers to solve them.
• Stay up to date with the latest advancements in AI/ML and implement best practices.
• Develop, implement, and maintain scalable machine learning models for various applications.

Design and Planning Data Engineering Solutions

• Design and implement testing frameworks to measure the impact of business interventions.
• Design and implement scalable, high-performance big data applications that support analytical and operational workloads.
• Lead evaluations and recommend best-fit technologies for real-time and batch data processing.
• Ensure that data solutions are optimised for performance, security, and scalability.
• Develop and maintain data models, schemas, and architecture blueprints for relational and big data environments.
• Ensure seamless data integration from multiple sources, leveraging Kafka for real-time streaming and event-driven architecture.
• Facilitate system design and review, ensuring compatibility with existing and future systems.
• Optimise data workflows, ETL/ELT pipelines, and distributed storage strategies.

Technical Development And Innovation

• Keep abreast of technological advancements in data science, data engineering, machine learning and AI.
• Continuously evaluate and experiment with new tools, libraries, and platforms to ensure that the team is using the most effective technologies.
• Lead end-to-end data science and data engineering projects that support strategic goals. This includes requirements gathering, technical deliverable planning, output quality and stakeholder management.
• Continuous research on to develop and implement innovative ideas and improved methods, systems and work processes which lead to higher quality and better results.
• Build and maintain Kafka-based streaming applications for real-time data ingestion, processing, and analytics.
• Design and implementation data lake and data warehouse data processing & ingestion applications.
• Utilise advanced SQL\Spark query optimisation techniques, indexing strategies, partitioning, and materialised views to enhance performance.
• Work extensively with relational databases (PostgreSQL, MySQL, SQL Server) and big data technologies (Hadoop, Spark).
• Design and implement data architectures that efficiently handle structured and unstructured data at scale.

Resourceful And Improving

• Find innovative ways following processes to overcome challenges, leveraging available tools, data, and methodologies effectively.
• Continuously seek out new techniques, best practices and emerging trends in Data Science, AI, and machine learning.
• Actively contribute to team learning by sharing insights, tools and approaches that improve overall performance.

Qualifications

Job Specification:

• At least 5 years in a technical role with experience in data warehousing, and data engineering.
• 3-5 years’ experience across the data science workflow will be advantageous
• 3-5 years of proven experience as a data scientist, with expertise in machine learning, statistical analysis and data visualisation will be advantageous.
• Proficiency in programming languages such as Python, Java, or Scala for data processing.
• Experience with big data technologies such as Hadoop, Spark, Hive, and Airflow, PostgreSQL, MySQL, SQL server
• Expertise in SQL/Spark performance tuning, database optimisation, and complex query development.
• Advantageous on .net Programming (C#, C++, Java) and Design Patterns.

Living the Spirit

• Adaptability & Resilience: Embrace change with flexibility, positivity, and a proactive mindset. Thrive in dynamic, fast-paced environments by adjusting to evolving priorities and technologies.
• Decision-Making & Accountability: Make timely, data-informed decisions involving the team to ensure transparency and alignment. Confidently justify choices based on thorough analysis and sound judgment.
• Innovation & Continuous Learning: Actively pursue new tools, techniques, and best practices in Data Science, AI, and engineering. Share insights openly to foster team growth and continuously improve performance.
• Collaboration & Inclusion: Foster open communication and create a supportive, inclusive environment where diverse perspectives are valued. Empower team members to share ideas, seek help, and give constructive feedback freely.
• Leadership & Growth: Lead authentically with integrity and openness. Support team members through mentorship, skill development, and creating a safe space for honest feedback and innovation. Celebrate successes and embrace challenges as growth opportunities.