Simple Machines. Data Engineered to Life™ 

Simple Machines is a leading independent boutique technology firm with a global presence, including teams in London, Sydney, San Francisco, and New Zealand. We specialise in creating technology solutions at the intersection of data, AI, machine learning, data engineering, and software engineering. Our mission is to help enterprises, technology companies, and governments better connect with and understand their organisations, their people, their customers, and citizens. We are a team of creative engineers and technologists dedicated to unleashing the potential of data in new and impactful ways. We design and build bespoke data platforms and unique software products, create and deploy intelligent systems, and bring engineering expertise to life by transforming data into actionable insights and tangible outcomes. We engineer data to life™. 

The Role: 

 
Join us at Simple Machines as a Senior Data Scientist, where you’ll spearhead pivotal projects that propel our clients' goals in machine learning and data science, creating notable commercial value. We’re looking for someone with a solid grounding in data science and AI, and a track record of effectively leveraging these tools in real-world business scenarios. You’ll thrive by balancing practical business needs with achievable solutions, all while keeping a keen eye on fostering strong client relationships and building your consulting expertise. 

Technical Responsibilities 

• Model Development and Selection: Develop and refine predictive models using advanced statistical techniques, with rigorous validation and testing to ensure model correctness. Be confident in choosing from and employing a broad range of tools, such as Tensorflow, XGBoost, or PyTorch where appropriate, and ensure model reproducibility with model management tools such as MLFlow and ModelDB. 
• Causal Analysis: Perform experiments and determine the causal relationships of defined interventions. Perform uplift modelling and optimise for business objectives. 
• Feature Engineering: Perform data extraction and feature engineering from warehoused data sources using SQL, and perform advanced feature engineering with Scala, Python, or R. Work with large datasets in Spark or BigQuery; and leverage advanced feature stores such as VertexAI and Tecton to manage and serve features efficiently. 
• Deployment and Integration: Deploy production models to the cloud and in containerised environments and set up integrations for both downstream systems such as PowerBI, and monitoring tools such as Grafana to ensure model performance over time. 
• Generative AI and LLMs: Integrate and monitor large language models like OpenAI's GPT-4 and Google's Gemini. 
• Pipeline automation: Continuously monitor model performance and automate data ingestion and model training with tools like Apache AirFlow and AWS Glue. 
• Safe and Ethical AI Practices: Uphold high ethical standards in AI applications by conducting regular audits for fairness and transparency, adhering to data privacy laws like GDPR, and promoting responsible AI use to align with societal norms and values. 
• Innovative Experimentation: Continuously explore new data sources and innovative algorithms to stay at the forefront of technological advancements in data science. 

Consulting Responsibilities:

• Strategic Advisory: Provide expert consultation to clients on leveraging data science for business advantage, including machine learning, predictive analytics, and data strategy. 
• Project Leadership: Manage data science projects, ensuring they align with business goals and are delivered on time and within budget. 
• Stakeholder Engagement: Effectively communicate complex data science concepts and results to non-technical stakeholders to support strategic decisions. 
• Innovation and Research: Stay at the forefront of data science technology and methods, applying cutting-edge research to practical business problems. 

Requirements

Ideal Skills and Experience:

• Technical Proficiency: Demonstratable expertise in programming languages such as Python, R, and SQL. Knowledge of feature transformation with big data frameworks like Apache Spark is required. A strong background in statistical analysis, machine learning algorithms, and data modelling techniques is required. 
• Tools and Platforms: Proficient in using leading data science platforms and libraries, such as TensorFlow or PyTorch for deep learning applications, and scikit-learn for implementing machine learning algorithms. Expertise with data visualization tools or libraries such as Tableau, matplotlib, or ggplot2 to derive insights and present data effectively. 
• Model Development and Selection: Extensive experience in developing and selecting statistical and machine learning algorithms tailored to specific business requirements. This involves not only creating models but also understanding and choosing the right algorithms to solve particular business problems effectively, ensuring alignment with strategic objectives. 
• Deep Theoretical Understanding: Possess a strong theoretical foundation in the principles and concepts of Data Science, Machine Learning, and Statistics. This knowledge should include an understanding of underlying algorithms, probability theory and statistics, and their practical applications in solving complex problems. 
• Safe and Ethical AI Practices: Proficiency in implementing safe and ethical AI practices, including knowledge of privacy-preserving techniques, bias detection and mitigation in AI models, and adherence to global data protection regulations such as GDPR. Commitment to the ethical use of data and AI technologies, ensuring transparency, accountability, and fairness in all data-driven initiatives. 

Education and Professional Experience: 

• Educational Background: A Master’s or PhD in Data Science, Computer Science, Statistics, or a related field is highly preferred. 
• Professional Experience: At least 5+ years of experience in data roles, with a proven track record of leading data science projects that have a significant business impact. 

Benefits

What We Offer in the UK: 

• Salary: Competitive salary and benefits package. 

• Pension: Up to 5% employer contribution, matching up to a 5% employee contribution, for a total of up to 10%. 
• Annual Leave: 4 weeks standard + 1 week additional annual leave over Christmas shut down period, plus public holidays. 
• Your Day - No Questions Asked: One additional day off per year, no explanation required! 
• Regular Lunches: Provided at team meet-ups and on workdays at Simple Machines' co-working space. 
• Health and Wellbeing Allowance: £1,250 allowance per year to be used for any food and non-alcoholic beverages during business hours, healthcare, gym memberships, sporting goods and accessories, and any wellness appointments. 
• Professional Development: £1,500 annual budget for training, courses, and conferences, with potential for additional funding. 
• Certifications: £2,500 annual budget for certifications and related courses. 
• Equipment Allowance: £1,500 for UK team members, plus Apple MacBook Pro laptops and necessary accessories. 
• Company Sick Leave: 10 days per annum, includes coverage for employee’s family. 
• Antenatal Support: Paid time off for antenatal appointments, including classes recommended by health professionals. 
• Terminal Illness Benefit: Three months' continuance of salary at full pay. 

Join Us: 

Simple Machines is a diverse and globally distributed team of individual talents. Everyone in the firm is among the best at what they do. That’s why they’re here. We have a collective obsession with the future and a passion to create real change through technology. If you’re someone who’s as passionate as we are about building a world-class technology company specialising in engineering for data, you’ll fit right in.