Data Scientist at SoluGrowth Pty Ltd

Description

• Collecting, analyzing and interpreting large datasets to extract valuable insight.
• As a Data Scientist, you will be a vital member of our financial consulting team.
• opportunity to leverage your analytical skills and technical expertise to extract valuable insights from complex financial data and solve complex challenges while contributing to driving impactful business decisions.
• This role offers a unique opportunity to apply data science techniques to real-world financial challenges and contribute to innovative solutions.
• collaborating with talented team members across various departments within the organization as you add value to clients.
• Clean, prepare, and analyse large datasets from various financial sources (e.g., banks, insurance companies, investment firms).
• Conduct exploratory data analysis to identify patterns, trends, and anomalies.
• Utilize statistical techniques (e.g., regression, hypothesis testing) to assess relationships between variables.
• Utilize Python, R, and SAS to analyze large datasets, extract actionable insights, and present findings to key stakeholders.
• Collaborate with cross-functional teams to integrate data science solutions into business strategies and operational workflows
• Develop and implement predictive models (e.g., machine learning algorithms) to forecast financial metrics (e.g., risk, returns, customer behaviour).Develop and implement machine learning models to solve complex business problems and enhance decision-making processes.
• Evaluate model performance using appropriate metrics and refine as needed.

Requirements

• Bachelor’s degree in data science, Computer Science, Statistics, Mathematics, or a related field (Master’s degree preferred)
  1- 2 years of experience in Data Science and machine learning (advantageous)
• Understanding of statistical concepts and machine learning algorithms.
• Familiarity with cloud platforms (e.g. AWS, Azure) and data warehousing tools
• Knowledge of financial and predictive modelling techniques (e.g. time series analysis, option pricing).
• Knowledge of deep learning frameworks and modality is preferred.