Associate Machine Learning Scientist

Job description

As Associate Machine Learning Scientist you will work on advanced research and development, to enhance relationships with clients, acquire strategic datasets and provide new solutions through product prototyping. You will concentrate on new data asset evaluation and acquisition and new product prototyping. This will involve inventing and modifying advanced innovative algorithms, and applying them to large datasets to solve difficult business problems.

You will balance and prioritise multiple concurrent projects, anticipate obstacles, and make high quality deliveries. You will report to Director of Data Science and Gen AI.

What you'll do

• Analyze, evaluate, and document large data sets
• Identify and apply appropriate machine learning/data mining/text mining techniques to uncover the value of the data
• Design data structure and data storage schemes for efficient data manipulation and information retrieval
• Develop tools for data processing and information retrieval
• Develop data-driven models to measure the value of a given data set
• Validate score performance Conduct ROI and benefit analysis
• Document and present model process and model performance
• Apply analytical and industry knowledge to construct analytical solutions with details for internal and external clients to solve their business problems.

Be connected to clients, competitors, relevant market groups, to be aware of and understand movements, trends, up-coming opportunities and risks.

Qualifications

What you'll bring

Grasp of probability, statistical inference, optimization algorithms, linear algebra, and calculus. Understanding of how and when to apply necessary analytical approaches including regression analysis, machine learning, AI, deep learning and other advanced modeling approaches to extract and automate insights from data. Grasp of techniques for model cross-validation and penalizing unnecessary complexity, including regularization, Bayesian shrinkage, in-sample vs out-of-sample.

Fluency with statistical significance tests and Monte Carlo simulation. Experience writing near-production-level code in at least one general purpose programming language (e.g. Python, C++). Understanding of Apache family (Hadoop, Spark), latest database technologies (e.g. Graph databases, time-series databases), and natural language processing and other tools for the parsing of unstructured data.