Data Scientist at Burgiss

About the role:

The Junior Data Scientist is responsible for identifying valuable data sources and automating collection processes; data wrangling; analyzing data to discover trends and patterns; designing, developing, fine- tuning and re-training ML and deep learning (DL) systems; conducting machine learning tests and experiments; implementing appropriate ML (and, when applicable, rules-based) algorithms; proposing solutions and strategies to business challenges and collaborating with engineering and product development teams.

Responsibilities:

• Identify valuable data sources and automate collection processes Undertake data wrangling.
• Present information using data visualization techniques.
• Design and build efficient self-learning applications and artificial intelligence products in collaboration with Data Operations, Engineering and Product Management teams.
• Combine models through ensemble modeling.
• Study and transform data science prototypes.
• Research and implement appropriate ML architectures, algorithms and tools.
• Produce project outcomes and isolate the issues that need to be resolved, to make programs more effective.
• Select appropriate datasets and data representation methods.
• Run machine learning tests and experiments.
• Perform self-audit and other quality assurance functions.
• Perform statistical analyses and fine-tuning using test results.
• Train and retrain systems when necessary.
• Extend existing ML libraries and frameworks.
• Research and implement best practices to improve the existing machine learning infrastructure.
• Keep abreast of developments in the field.
• Build algorithms based on statistical modeling procedures and build and maintain scalable machine learning solutions in production.
• Use data modeling and evaluation strategy to find patterns and predict unseen instances.
• Communicate and explain complex processes to people who are not programming experts.
• Liaise with stakeholders to analyze business problems, clarify requirements, and define the scope of the resolution needed.
• Analyze large, complex datasets to extract insights and decide on the appropriate technique.
• Collaborate with data engineers to build data and model pipelines.
• Meet personal and team deadlines through time management and collaboration with team members.

Required Qualifications & Skills:

• Minimum of two years of a proven work-related or academic experience in data science and machine learning
• Ability to write robust code in Python. Knowledge of R and Java is preferred but not required Familiarity with machine learning frameworks (such as PyTorch, Hugging Face, TensorFlow and Keras) and libraries (e.g., scikit-learn)
• Private equity and real estate experience preferred but not required
• Understanding of data structures, data modeling and software (ML and rules-based) architectures Outstanding analytical and problem-solving skills
• Deep knowledge of mathematics, probability, statistics, and algorithms
• Ability to prioritize tasks, work on multiple assignments simultaneously, and manage rapidly changing assignments in a team environment
• Workflow coordination experience
• Strong time management and critical thinking skills Highly self-motivated with strong attention to detail
• Excellent interpersonal and communication skills (written and verbal) A desire to work in a team environment, often under pressure Demonstrates high standards of ethics and fairness
• Advanced MS Excel and Word skills a plus
• Data Science and/or AI-related university degree is preferred but not required
• CAIA/CFA certification is preferred but not required