Intern Machine Learning

As a Machine Learning Intern, 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:

1. Query large datasets with SQL and feed ML models
2. Perform data exploration to find patterns in the data and understand the state and quality of the data available
3. Utilize Python code for analyzing data and building statistical models to solve specific business problems
4. Evaluate ML models and fine tune model parameters considering the business problem behind
5. Collaborate with senior peers to Deploy ML models in production
6. Build customer-facing reporting tools to provide insights and metrics which track system performance
7. Be part of and contribute towards a strong team culture and ambition to be on the cutting edge of big data
8. Participate in the off-hours on call stability rotation to support live ML models

Requirements

1. Bachelor's degree in AI, Statistics, Math, Operations Research, Engineering, Computer Science, or a related quantitative field
2. Statistical modelling and math
3. Basic knowledge of Machine learning algorithms
4. Basic knowledge of SQL
5. Basic knowledge of visualization tools such as Periscope
6. Excellent verbal and written communication
7. Strong problem-solving skills

Desired candidate profile

1. Data Preparation and Preprocessing
   1. Data Cleaning: Assist in preparing and cleaning data for machine learning models. This could include handling missing values, removing outliers, and converting data into appropriate formats.
   2. Feature Engineering: Help with creating new features or transforming existing data to improve the performance of machine learning algorithms.
   3. Data Exploration: Perform exploratory data analysis (EDA) to understand data distributions, identify trends, and visualize relationships in the data.
2. Model Building and Evaluation
   1. Model Implementation: Assist with the implementation of machine learning models such as linear regression, decision trees, support vector machines, and neural networks, using tools like scikit-learn, TensorFlow, or PyTorch.
   2. Model Training: Help train models using various datasets, tuning hyperparameters to optimize performance.
   3. Model Evaluation: Evaluate model performance using appropriate metrics like accuracy, precision, recall, F1 score, or AUC-ROC, and assist in interpreting results.
3. Algorithm Research and Testing
   1. Literature Review: Conduct research on the latest machine learning algorithms and approaches. You might be tasked with reviewing research papers and experiments to help implement cutting-edge methods in practice.
   2. Experimentation: Run experiments to test different machine learning algorithms, evaluate their performance, and understand how various approaches affect outcomes.
4. Collaboration and Reporting
   1. Team Collaboration: Work closely with senior data scientists, machine learning engineers, and other team members to develop machine learning models or contribute to data-driven projects.
   2. Documentation: Document your work, including code, findings, and explanations for model choices and outcomes, so that results can be easily interpreted and reproduced by others.
   3. Presentation: Present findings to the team, often through reports or short presentations, to share insights or progress on ongoing projects.
5. Tool and Software Usage
   1. Machine Learning Libraries: Gain experience using libraries and frameworks such as scikit-learn, TensorFlow, Keras, PyTorch, or XGBoost to implement and fine-tune machine learning models.
   2. Data Manipulation: Use tools like Pandas for data manipulation, NumPy for numerical computations, and Matplotlib or Seaborn for data visualization.
   3. Version Control: Use Git and GitHub for code version control, helping ensure that your work can be tracked and shared efficiently with team members.