Machine Learning Engineer

Roles and responsibilities

As the Machine Learning Engineer, you will be participating in exciting projects covering the end-to-end Data Science lifecycle - from raw data cleaning and exploration with primary and third-party systems, through advanced state-of-the-art data visualization and Machine learning development. You will work in a modern cloud-based data warehousing environment hosting Machine Learning models alongside a team of diverse, intense and interesting co-workers. You will liaise with other departments - such as product & tech, the core business verticals, trust & safety, finance and others - to enable them to be successful.

In this role, you will:

• Work on regression and classification problems on tabular, textual and image data
• Work on forecasting, anomaly detection and time-series analysis
• Build recommendation engines
• Work on GPT-based applications using stock models for various business use cases
• Query large datasets in AWS Redshift to extract the necessary data that will feed ML models
• Perform data exploration to find patterns in the data and understand the state and quality of the data available
• Utilize Python code for analyzing data and building statistical models to solve specific business problems
• Evaluate ML models and fine tune model parameters considering the business problem behind
• Collaborate with senior peers to Deploy ML models into production that work as standalone data services
• Build customer-facing reporting tools to provide insights and metrics which track system performance
• Participate in the off-hours on call stability rota to support live ML models
• Own at least one ML product that is in production

Requirements

• Master's degree in AI, Statistics, Math, Operations Research, Engineering, Computer Science, or a related quantitative field
• 2+ years of working experience in Machine Learning
• Experience with AWS is a plus
• Knowledge in Statistical modelling and maths
• Intermediate knowledge of Python's ML stack: Pandas, Matplotlib, Sklearn, Tensorflow
• Intermediate knowledge of machine learning algorithms such as Linear regression, Gradient boosted trees, Neural networks
• Basic knowledge of SQL, and visualization tools such as Periscope with experience in handling large datasets
• Basic knowledge of statistical analysis and A/B testing
• Excellent verbal and written communication
• Strong problem solving skills
• Analytical thinking; Conceptual thinking Detail-oriented; Business Acumen
• Entrepreneurial spirit and ability to think creatively; highly-driven and self-motivated; strong curiosity and strive for continuous learning

Desired candidate profile

. Developing and Implementing Machine Learning Models

• Model Development: Design and implement machine learning models to address specific business problems (e.g., classification, regression, clustering).
• Algorithm Selection: Choose appropriate algorithms based on the problem requirements, such as supervised, unsupervised, or reinforcement learning.
• Model Training and Evaluation: Train models on large datasets and evaluate their performance using metrics like accuracy, precision, recall, F1-score, or AUC (depending on the task).
• Model Optimization: Tune hyperparameters, adjust algorithms, and experiment with different architectures to improve model performance.

2. Data Processing and Feature Engineering

• Data Cleaning: Preprocess raw data, handle missing values, outliers, and ensure that the data is clean and suitable for machine learning.
• Feature Engineering: Extract relevant features from raw data, transforming it into a format suitable for modeling (e.g., scaling, encoding, dimensionality reduction).
• Data Integration: Combine data from different sources and ensure proper data flow into the machine learning pipeline.

3. Model Deployment and Integration

• Deploying Models: Work with software engineers to deploy machine learning models into production environments.
• API Development: Develop APIs for integrating machine learning models into larger systems or applications for real-time or batch inference.
• Scalability: Ensure that machine learning models can handle large-scale data and can be efficiently used in production environments (e.g., using distributed computing, cloud services like AWS, GCP, or Azure).
• Monitoring and Maintenance: Continuously monitor model performance in production, detecting issues like data drift or model degradation, and retrain models when necessary.

4. Collaboration with Cross-Functional Teams

• Working with Data Scientists: Collaborate with data scientists to understand the problem, select the right algorithms, and develop experimental models.
• Working with Software Engineers: Work with software engineers to integrate machine learning models into software products or services.
• Business Stakeholders: Translate business requirements into technical solutions and communicate findings to non-technical stakeholders.