DSTA Hiring! Full Time Data Scientist - Senior Data Scientist in - Ricebowl

The Chief Information Office is seeking motivated Data Scientists to join a young, agile and multi-disciplinary team. You will drive the identification, development and delivery of high-impact solutions by applying a product-centric mindset, leveraging data, AI, advanced analytics and deep learning techniques to address real-world problems and deliver business value to DSTA.

Job Responsibilities

• Evaluate the feasibility and value of AI and data use cases, taking into account data availability and quality, technical constraints, responsible AI, security and implementation considerations.
• Translate business problems into analytical and machine learning models.
• Build complex models and conduct exploratory analysis on large datasets to derive actionable insights.
• Establish effective partnerships with data engineering and data science platform teams to develop and integrate data pipelines.
• Develop and deploy data-driven solutions to improve decision-making and deliver business value to DSTA.
• Drive experimentation, prototyping and iterative validation to establish value before committing to full-scale product development.
• Degree in Data Analytics, Computer Science, Information Systems, Computer Engineering or related disciplines.
• For the Senior Data Scientist role, at least 2 years of experience in developing and deploying data analytics and/or artificial intelligence solutions.
• Demonstrated expertise in data manipulation, exploratory analysis and model development.
• Strong understanding of machine learning, deep learning and Generative AI/LLMs, including the ability to select appropriate techniques and models for different use cases.
• Proficiency in Python, R and SQL.
• Experience in product and problem framing.
• Strong communication and interpersonal skills, with the ability to work effectively in a multi-disciplinary team.
  
  

Competencies Required

• Ability to translate business problems into technical and analytical models.
• Expertise in advanced analytics, machine learning, deep learning and AI techniques.
• Strong communication and multi-disciplinary collaboration skills.
• Adaptability to work effectively in a dynamic and multi-disciplinary environment.