DREDGING DATA SCIENTIST

Company : Nmdc

Job Type : Full Time

Number of Applicants

:

000+

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Job Description - DREDGING DATA SCIENTIST

Key Accountabilities:

A Dredging Data Scientist analyses all dredging related data generated during dredging projects with the help of databases, programming and visual dashboards.
The scientist collects and stores data, checks completeness and accuracy, implements analyses while ensuring statistical quality and generates reports for the management.

Advanced analytics use cases successfully deployed code and deploying them into production
Support in the development of analytics standards and protocols to ensure scalability and support self-service advanced analytics.
Research and stay up to date on latest data science and analytics approaches and practices in the market ensuring that, when relevant, these are appropriately leveraged within Data and Analytics function.
Implemented standards and protocols as per prescribed project requirement.

Responsibilities and duties:

Apply advanced statistical and predictive modelling techniques to build, maintain, and improve on multiple decision systems, Designs experiments, and test business hypotheses.
Identify what data is available and relevant in the company for analytics opportunities, including internal and external data sources, leveraging new data collection processes such as geo-location data, Unstructured data sources such emails, log files, social media etc.
Mine and analyse data from company databases to drive optimization and improvement in dredging process development, project enhancement and contract budget estimation.
Develop innovative and effective approaches to solve business problems using analytics and communicate results and methodologies.
Utilize patterns and variations from existing dredging database and provide predictive analysis outputs.
Provide on-going tracking and monitoring of performance of decision systems and statistical models built by the data science unit.
Recommend & Implement ongoing improvements to methods and algorithms that lead to findings, including new information & patterns in the data.
Establish conducive ecosystem for analytics using tools, processes, contracts etc. within the company that will enable adoption of analytics across the organization.
Play the role of company wide machine learning and Artificial Intelligence expert to use these techniques to solve the real dredging project problems.

Professional Skill Requirements

Experience in "design and prototype" part of the ML development pipeline, beginning with pulling datasets from SQL and ending with serializing ML models and assisting production engineers to productionize model retraining and model serving systems.
Practical experience of running impactful inferential analyses and data investigations to identify recurring patterns, root causes, and propose actionable product solutions.
Must know to design approaches to validate findings or test hypotheses, interpret experiments to measure the impact of new features.
Collect and store dredging related data in a database and check periodically their completeness and accuracy through an effective communication with project teams.
Using of AI and building machine-learning algorithms to achieve specific goals, develop efficient routines and methods for the project related tasks.

Experience:

Experience using statistical computer languages (R, Python, SLQ, etc.) to manipulate data and draw insights from large data sets.
Experience working with and creating data architectures.
Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.
Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc.
Experience querying databases and using statistical computer languages: R, Python, SLQ, etc.
Experience using web services: Redshift, S3, Spark, Digital Ocean, etc.
Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modelling, clustering, decision trees, neural networks, etc.

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