The primary purpose of this position is to lead the Machine Learning practice in Diaceutics, helping the team to analyse current data sets and identify supportive data sets for the development of new products using cutting edge tools and algorithms.
The candidate must be passionate about supporting evidence-based decision making and driven to investigate patterns and relationships within data using systematic and scalable methods.
Duties & Responsibilities:
- Identify evidence needs & recommend data solutions; ask the right scientific questions, understand the evidence needs for research and development, regulatory and market access, and identify and make recommendations on fit-for-purpose data and analytics solutions.
- Dive into data: Develop a comprehensive and deep understanding of the data we work with using AWS analytical tools and applications to broaden data accessibility and advance our proficiency/efficiency in understanding and using the data appropriately.
- Be an expert in applying methods: Stay current with and adopt emergent analytical methodologies, tools and techniques to ensure fit-for-purpose and impactful approaches.
- Produce high quality analyses: Apply rigor in study design and analytical methods; plan for data processing; design a fit-for-purpose analysis plan, assess effective ways of presenting and delivering the results to maximize impact and interpret-ability; implement and/or oversee the study, including its reporting; ensure compliance with applicable pharma industry regulations and standards.
- Interpret and share results: Communicate findings to the rest of the team, internal stakeholders and regulatory body if needed.
- Demonstrated strong collaboration skills and excellent communication skills to work with a global virtual team.
- Code with integrity. Demonstrate a commitment to documentation, review, and testing.
- Keep up to date with standards for data privacy to ensure integrity and accountability in all aspects of analytics and machine learning.
- Take an approach to analysis that takes the right balance of privacy for the data subject vs getting the best result.
- Coordinate analytical activities with the broader activities within the Global Data team.
- Assisting in hiring more junior members into the team and mentoring them.
General Education & Experience:
- Education – MSc or PhD in Statistics, Mathematics, Computer Science or relevant field
- Experience –
- Strong Hands-on experience with Amazon SageMarker and AWS
- Deep understanding of the Machine Learning Principles. Clustering, SVM, Regressions, Dimensionality Reduction, Bayesian Networks, classification, over-fitting
- Deep understanding of the Machine Learning Principles. Clustering, SVM, Regressions, Dimensionality Reduction, Bayesian Networks, classification, over-fitting concept
- Champion data collection from all sort of sources and familiar with the popular ETL and data integration tools
- Fluency with one of the following: Python, R, Weka, Matlab, Octave
- Experience in the Pharmaceuticals and Biotechnology. Interpret molecular variants from clinical testing results and link molecular variants to disease through public and private reference data, including through database APIs
- Demonstrated experience with managing project scope and driving delivery in an evolving environment requiring proactivity and effective problem-solving
3. Computer and Software Knowledge and Skills – Proficient skills in Microsoft Office Suite. Proficient skills in Microsoft Office Suite (advanced PowerPoint user preferred.)
4. Virtual Communication – Demonstrated high-end expertise as user of video call technology and global teleconferencing.
Compensation and benefits
Diaceutics will offer the successful candidate an attractive remuneration package. The successful candidate will receive a monthly base compensation.