Senior Data Scientist

Lumilinks Group Ltd; London; On-site; GBP 70,000 - 90,000; Full time

Job summary

As a Lead Data Scientist at a leading tech company, you will shape data science initiatives and drive business solutions. Your expertise in machine learning and statistical analysis will guide innovative projects, transforming data into actionable insights. Join us to make a significant impact in the field and lead a talented team towards success.

Qualifications

• 5+ years of experience in data science or analytics.
• Strong programming skills in Python, SQL, and data visualization tools.
• Experience in mentoring juniors and leading projects.

Responsibilities

• Lead data science projects from inception to execution.
• Mentor junior teams and support their professional development.
• Develop complex models and implement machine learning algorithms.

Skills

Machine Learning

Statistical Analysis

Data Visualization

Data Strategy

Education

Master's degree in Data Science

Ph.D. in relevant field

Tools

Python

SQL

Machine Learning Frameworks

Data Visualization Tools

Job description

1 week ago Be among the first 25 applicants

As a Lead (Senior) Data Scientist at Lumilinks, you will take a leadership role in shaping our data science initiatives and driving impactful solutions that align with our strategic objectives.

In this position, you will leverage your extensive experience and advanced analytical skills to guide the development of complex models, lead cross-functional projects, and mentor a team of data scientists. You will play a crucial role in transforming raw data into actionable insights that support key business decisions and enhance our product offerings.

You will collaborate closely with stakeholders across various departments, including engineering, product development, and business strategy, to identify opportunities for leveraging data to drive innovation and growth. Your expertise in machine learning, statistical analysis, and data visualisation will enable you to design and implement robust analytical frameworks that enhance our understanding of customer behaviour, market trends, and operational efficiency.

This is an exciting opportunity to contribute to the expansion of our data science company and make a significant impact in the field.

The Day Job

• Project Leadership: Leading data science projects from conception to execution, defining project goals, timelines, and deliverables, and ensuring alignment with business objectives and Lumilinks product descriptions.
• Team Management and Mentorship: Guiding and mentoring junior data scientists and associates, providing support in their professional development, and creating a collaborative team environment.
• Advanced Model Development: Designing and implementing complex statistical models and machine learning algorithms that solve critical business problems, while ensuring scalability and robustness.
• Strategic Data Insights: Translating data findings into strategic insights and recommendations for stakeholders, contributing to data-driven decision-making at the executive level.
• Collaboration with Cross-Functional Teams: Working closely with product managers, engineers, and other departments to understand their needs, develop data-driven solutions, and ensure successful project integration.
• Data Strategy Development: Contributing to the formulation of the company’s overall data strategy, identifying opportunities for leveraging data to enhance products, services, and operational efficiency.
• Research and Innovation: Staying at the forefront of industry trends, technologies, and methodologies, and applying innovative approaches to improve data science practices within Lumilinks.
• Performance Monitoring and Optimisation: Establishing metrics to monitor the performance of deployed models and analytics solutions, and making iterative improvements based on feedback and performance data.
• Stakeholder Communication: Presenting insights, findings, and recommendations to non-technical audiences, including executives and board members, in a clear and compelling manner.
• Documentation and Knowledge Sharing: Maintaining comprehensive documentation of methodologies, project outcomes, and best practices, and facilitating knowledge sharing within the team and across the organisation.

Please speak to us if you have…

…the following professional aspirations

• Innovative Projects: Eager to lead cutting-edge projects that utilise advanced machine learning techniques, contributing to the company's competitive advantage and innovation.
• Thought Leadership: Aiming to establish oneself as a thought leader in the data science community through publications, conference presentations, or participation in industry panels.
• Building Expertise: Committed to deepening expertise in specific areas of data science, such as deep learning, natural language processing, or big data technologies, to drive specialised initiatives.
• Cross-Departmental Influence: Seeking to expand influence beyond the data science team, collaborating with other departments to create a data-driven culture throughout Lumilinks.
• Mentorship and Training Development: Aspiring to create training and mentorship programs for junior data scientists, nurturing talent and building a strong data science pipeline within Lumilinks.
• Data Governance Advocacy: Aiming to champion best practices in data governance and ethics, ensuring responsible and transparent use of data within the company.
• Impactful Business Contributions: Desiring to lead initiatives that directly contribute to improved business outcomes, such as customer satisfaction, revenue growth, or operational efficiency.
• Global Impact: Interested in working on data science initiatives that address global challenges or have a significant societal impact, potentially collaborating with other organisations or sectors.

…the following personal attributes

• Analytical Mindset: Strong ability to analyse complex problems and datasets, drawing insights that drive decision-making.
• Curiosity: A natural inclination to explore new technologies, methodologies, and trends in data science and machine learning, seeking to expand knowledge continuously.
• Effective Communicator: Skilled at conveying complex technical concepts to both technical and non-technical audiences, ensuring clarity and understanding among stakeholders.
• Collaborative Spirit: Enjoys working with cross-functional teams, valuing diverse perspectives, and creating a cooperative work environment.
• Mentorship Orientation: A desire to guide and support junior team members, sharing knowledge and providing constructive feedback to help others grow.
• Problem Solver: A proactive approach to identifying challenges and developing innovative solutions, with a focus on practical applications of data science.
• Adaptability: Comfortable with change and uncertainty, able to pivot quickly in response to new information or shifting business priorities.
• Attention to Detail: A meticulous approach to data analysis and model development, ensuring accuracy and reliability in results.
• Resilience: Ability to navigate setbacks and challenges with a positive attitude, encouraging persistence within the team.
• Visionary: A strategic thinker who can envision the potential future applications of data science and how they can benefit Lumilinks.

…the following technical skills and knowledge

• Advanced Programming Skills:
• Python: Mastery of Python for machine learning, data manipulation, and automation, with extensive use of libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, and PyTorch.
• SQL : Advanced knowledge of SQL for manipulating and querying data, feature generation and functions (in particular Snowflake’s SQL dialect)
• Data Manipulation and Management:
• Expert-level skills in data preprocessing, feature engineering, and data transformation techniques using SQL,Pandas and other technologies.
• Experience with data warehousing and database technologies (e.g., PostgreSQL, MySQL, NoSQL databases like MongoDB).
• Statistical Analysis and Experimental Design:
• Deep understanding of statistical methods, including advanced topics such as Bayesian statistics, multivariate analysis, and causal inference.
• Ability to design and analyse experiments (A/B testing) to evaluate model performance and business impact.
• Machine Learning and AI:
• Extensive experience with a wide range of machine learning algorithms, including supervised, unsupervised, and reinforcement learning techniques.
• Knowledge of deep learning architectures (e.g., CNNs, RNNs) and natural language processing (NLP) techniques.
• Data Visualisation and Communication:
• Advanced skills in data visualisation using tools such as Tableau, Power BI, or libraries like Matplotlib and Seaborn to effectively communicate insights to stakeholders.
• Ability to present complex technical concepts to non-technical audiences clearly and effectively.
• Big Data Technologies: Familiarity with big data frameworks such as Apache Spark, Hadoop, or distributed computing concepts for processing large datasets.
• Cloud Computing and Infrastructure: Proficient in cloud platforms (e.g., AWS, Google Cloud, Azure) for data storage, processing, and model deployment, including services like AWS SageMaker, Google Cloud ML Engine, or Azure Machine Learning.
• Model Deployment and Monitoring: Experience in deploying machine learning models in production environments, including knowledge of MLOps practices for model monitoring, versioning, and maintenance.
• Data Privacy and Ethics: Understanding of data privacy regulations (e.g., GDPR, CCPA) and ethical considerations in data science to ensure responsible data usage.
• Leadership and Mentorship: Proven ability to lead and mentor junior data scientists, creating a collaborative and innovative team environment.
• Data Ethics: Ensuring compliance with data privacy regulations and ethical standards in data handling and analysis and advocating for best practices.

…and the following experience, accreditations, and qualifications

• Educational Background: A master's degree or Ph.D. in a relevant field such as Data Science, Computer Science, Statistics, Mathematics, or a related discipline.
• Professional Experience: Significant experience (typically 5+ years) in data science, analytics, or related roles, with a proven track record of successful projects and leadership in a start-up or dynamic environment.
• Project Portfolio: A robust portfolio showcasing end-to-end data science projects, including model development, deployment, and business impact, as well as contributions to open-source projects or publications.
• Certifications: Relevant certifications from recognised institutions or platforms (e.g., Coursera, edX, DataCamp) in data science, machine learning, or specific tools and technologies (e.g., AWS Certified Data Analytics, Google Professional Data Engineer).

Seniority level

• Seniority level

  Mid-Senior level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Information Technology and Engineering

• Industries

  Software Development

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