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About us: Turning the fantasy of analytics, data and A.I. into reality….
In a world where vast amounts of data are being created in a multitude of different ways, Lumilinks exist to help companies collate and use data in an automated and compliant way, creating live and actionable insights.
We help businesses across the entire data journey, eliminating silos and creating data transparency. This allows our clients to be data confident in making strategic and tactical decisions that will further their business and create automation that improves processes, compliance, capability and reduces costs.
The Role
As a Data Scientist at Lumilinks, you will be at the forefront of our mission to leverage data-driven insights to solve complex problems and deliver innovative solutions. In this pivotal role, you will work collaboratively with a diverse team of data engineers, product managers, and data analysts to translate raw data into actionable strategies that drive growth and enhance our product offerings.
You will be responsible for a wide range of activities, from collecting and pre-processing data to developing predictive models and generating insightful visualisations. Your analytical skills will be essential in identifying trends, patterns, and anomalies within data, enabling us to make informed decisions that align with our business objectives.
In a fast-paced start-up environment, you will have the opportunity to take ownership of projects from inception to execution, experimenting with new methodologies and technologies. Your contributions will directly impact the direction of our products and services, offering you a unique chance to shape the future of our company.
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
- Data Analysis and Interpretation: Analysing large datasets to extract meaningful insights and translating complex data findings into actionable recommendations for stakeholders.
- Model Development and Implementation: Designing, developing, and validating machine learning models and statistical algorithms to address specific business challenges and improve decision-making processes.
- Data Cleaning and Pre-processing: Ensuring data quality by cleaning and pre-processing raw data, handling missing values, and transforming data into a suitable format for analysis.
- Feature Engineering: Identifying and creating new features from existing data that enhance the performance of predictive models.
- Collaboration with Stakeholders: Working closely with cross-functional teams, including product managers, engineers, and marketing, to understand business needs and align data science efforts with company goals.
- Data Visualisation: Creating compelling visualisations and dashboards using tools like Tableau, Power BI, or Python libraries (e.g., Matplotlib, Seaborn) to effectively communicate insights to both technical and non-technical audiences.
- Experimentation and A/B Testing: Designing and conducting experiments to test hypotheses and measure the impact of changes in products or services, providing data-driven recommendations based on results.
- Deployment of Models: Collaborating with engineering teams to deploy data models into production, ensuring they are scalable and maintainable, and monitoring their performance over time.
- Staying Updated with Trends: Continuously researching and learning about new technologies, methodologies, and industry trends to apply best practices in data science and enhance the company’s capabilities.
- Documentation: Maintaining thorough documentation of processes, methodologies, and findings to ensure transparency, reproducibility, and knowledge transfer within the team.
- Mentoring and Training: Providing guidance to junior team members or colleagues interested in data science, building a collaborative learning environment.
- Contributing to Data Strategy: Participating in the development of the company’s data strategy, identifying opportunities for leveraging data to drive growth and innovation.
Please speak to us if you have …..
…..the following professional aspirations
- Skill Development: Eager to expand technical skills in areas such as machine learning, deep learning, natural language processing, or big data technologies to enhance expertise and effectiveness.
- Project Ownership: Aspiring to take on more responsibility in leading data science projects, from conception to implementation, and driving impactful outcomes for the business.
- Cross-Functional Collaboration: Seeking opportunities to collaborate with various teams (such as engineering, product management, and marketing) to better understand their data needs and contribute to a data-driven culture.
- Mentorship and Growth: Aiming to eventually mentor junior data scientists, helping to nurture talent and contribute to the development of the data science community within the Lumilinks.
- Thought Leadership: Aspiring to establish a reputation as a knowledgeable contributor in the data science field.
- Innovative Solutions: Interested in working on ground-breaking projects that leverage data science to solve complex problems, drive innovation, and contribute to the company's competitive advantage.
- Career Advancement: Aspiring to progress to a more senior role, such as a lead data scientist or data science manager, to take on greater leadership responsibilities and influence the direction of data initiatives.
- Impactful Contributions: Eager to contribute to projects that have a significant positive impact on business outcomes, such as improving customer experience, increasing efficiency, or driving revenue growth.
- Building a Data-Driven Culture: Seeking to help build a culture of data-driven decision-making within Lumilinks, advocating for the use of data insights across all levels of the business.
- Exploring New Technologies: Aspiring to work with cutting-edge tools and technologies in data science, continuously exploring new methodologies that can enhance analytical capabilities and business outcomes.
….. the following personal attributes
- Curiosity: A strong desire to explore data and uncover insights, constantly asking questions and seeking to understand the underlying patterns in the data.
- Analytical Thinking: Ability to approach problems logically, breaking down complex issues into smaller, manageable components for effective analysis.
- Adaptability: Willingness to embrace change and pivot quickly in response to new information, challenges, or shifting business priorities, which is often necessary in a start-up environment.
- Collaboration: A team player who values input from colleagues, enjoys working with others across various departments, and contributes to a positive team dynamic.
- Attention to Detail: A meticulous approach to data cleaning, analysis, and model development, ensuring accuracy and reliability in results.
- Problem-Solving Mindset: Strong ability to identify challenges and develop innovative solutions, leveraging data science techniques to address business problems.
- Effective Communicator: Proficient in conveying technical concepts and insights to non-technical stakeholders, ensuring clarity and understanding across Lumilinks.
- Resilience: Ability to handle setbacks and challenges with a positive attitude, maintaining motivation and focus on achieving goals.
- Passion for Learning: A commitment to continuous professional development, staying updated with the latest trends, technologies, and methodologies in data science.
- Ethical Awareness: A strong understanding of ethical considerations in data handling and analysis, ensuring responsible use of data and adherence to privacy standards.
…the following technical skills and knowledge
- Programming Languages: Python: Proficient in Python for data analysis, machine learning, and automation. Familiarity with libraries such as Pandas, NumPy, Scikit-learn, and TensorFlow or PyTorch. R: Experience with R for statistical analysis and data visualisation.
- Data Manipulation and Analysis: Advanced skills in data pre-processing, cleaning, and manipulation using tools like SQL and Pandas. Proficient in data exploration techniques to identify trends, patterns, and anomalies.
- Statistical Knowledge: Strong understanding of statistical concepts, including probability, distributions, hypothesis testing, and regression analysis.
- Machine Learning: Experience with a range of machine learning algorithms (e.g., linear regression, decision trees, support vector machines, clustering) and understanding of model evaluation techniques (e.g., cross-validation, ROC-AUC). Familiarity with advanced techniques such as deep learning, natural language processing, or reinforcement learning.
- Data Visualisation: Proficient in data visualisation tools and libraries (e.g., Matplotlib, Seaborn, Tableau, or Power BI) to effectively communicate insights and findings.
- Big Data Technologies: Familiarity with big data processing frameworks such as Apache Spark, Hadoop, or distributed computing concepts.
- Cloud Computing: Experience with cloud platforms (e.g., AWS, Google Cloud, Azure) for data storage and model deployment, including knowledge of services like AWS S3, Lambda, or Google BigQuery.
- Version Control: Strong understanding of version control systems, particularly Git, for collaborative development and code management.
- Data Pipeline Development: Experience in designing, building, and maintaining data pipelines for ETL (Extract, Transform, Load) processes.
- Business Acumen: Understanding of the industry and market dynamics relevant to the start-up, allowing for informed data-driven decision-making.
