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Lead Data Scientist at Rivi Consultancy Services Private Limited

Let's talk about Responsibilities:

â Research, customization, and development of statistical and machine learning algorithms to

meet unique and complex project requirements that have broad business impacts; tasks

include defining hypotheses, executing necessary tests and experiments, evaluating, tuning

and optimizing algorithms and methods to specific situations.

â Analysis of big data for data-driven solution validation, evaluation and technology

innovation.

â Optimize data analysis processes and systems for better efficiency and maintainability.

â Coordinate with different functional teams to implement models and monitor outcomes.

â Leading project teams to achieve milestones and objectives.

â Anticipating challenges and issues and recommending process, product, and service

improvements.

â Work with stakeholders throughout the organization to identify opportunities for leveraging

company data to drive business solutions.

â Collaborate with management, stakeholders, and teams to define technology roadmaps.

â Mentoring and training more junior team members and serving as a best-practice resource

for statistics, artificial intelligence, and machine learning.

â Writing documents that clearly explain how algorithms should be implemented, verified,

and validated.

â Writing documents for use in the preparation of intellectual property and technical

publications.

â Monitoring the literature of interest and industrial development trends broadly in the areas

of data analysis and machine learning.

â Understanding regulatory requirements, such as those mandated by the FDA.

â Working within the Quality system, standards and maintaining training requirements.

â Being ever mindful of the requirements of the broader market and stakeholders.

â Promoting safe working environment within OH&S guidelines



Let's talk about Qualifications and Experience:

â Expert in statistical analysis methods, including analysis of variance, regression, time series

analysis, survival analysis, etc.

â Expert knowledge in artificial intelligence and machine learning fundamental theories and

data mining technologies.

â Thorough knowledge of data engineering or informatics systems.

â Leadership and hands-on experience with the development of data analytics systems,

including data exploration/crawling, feature engineering, model building, performance

evaluation, and online deployment of models.

â Proficient with R programming and server-side programming in Python or Java.

â Hands on experience in handling large and distributed datasets on Hadoop, Spark, Hive, Pig

or Storm, etc.

â Strong database skills and experience, including experience with SQL programming.

â Knowledge in big data technologies, including cloud computing/distributed computing, data

fusion, and data visualization.

â A background in or exposure to biomedical engineering, outcomes research, medical

science, or physiology.

â Good technical writing and presentation skills.

â Optimization of algorithm complexity vs. accuracy vs. implementation cost.

â Implementing robust software for use in research programs with a minimum of review and

other formal processes.

â A degree in Computer Science, Engineering, Statistics, Applied Mathematics, or related

fields. Minimal 8 years' industry or academic experience in data science.

â Post-graduate research experience (Masters or PhD) in a field encompassing Data Science,

Applied Statistics, Biomedical Informatics, or Outcomes research.

â Relevant industry experience would be favourably considered.



10.00-17.00 Years

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by Tsutomu Narushima