Job Location: Gurgaon/Gurugram
Position Description:
We are looking for an adaptable and inquisitive data scientist that will help us discover information hidden in vast and wide amounts of real-world data and help us make smarter decisions. The data scientists primary focus will be in applying data mining techniques, doing statistical analysis, and building high quality prediction and inference systems.
We are assembling a global, diverse team to build a new foundation for advanced data science practice. This foundation will usher in a wave of innovation that to take full advantage of leveraging modern data stacks in the cloud.
The data scientist will have the opportunity to guide, measure, and contribute to our data ecosystems, as well as to broader data science and analytics initiatives. CNH Industrial is fundamentally data-driven, and this role is at the core of defining and communicating what drives us strategically. They will work closely with development teams to define performance metrics, report on performance, and collaborate with others implementing solutions in production. In addition, the data scientist may be identified as an expert in a particular analytic approach, associated computational, or in the application of analytics in a particular business domain. They will have good networks within the technical community to enable them to collaborate on technical solutions, obtain resources and cooperation needed, and remove roadblocks so that they can ensure the success of their team.
The data scientist demonstrates very good communication skills and the ability to explain conclusions to customers with limited knowledge and experience with quantitative analytical methods. The data scientist will be expected to have previous experience as the technical lead on project teams, and should exhibit strong communication skills, a penchant for innovation, and above average initiative, people development, and interpersonal skills. They should also have a comprehensive knowledge of CNH Industrial; its products and services; its internal systems, processes, and procedures; and a general understanding of CNH Industrials external customers, competitors, and market influencers.
Job responsibilities/ details:
Typical assignments include projects aimed to make improvements in inventory management; optimizing supply chains and logistics; assurance of supply; forecasting procurement trends; predicting quality related metrics; and inferring contributors to marketing churn.
Significant challenges of this position are working with people; fostering the teams creativity; maintaining knowledge of approaches used in similar projects; and pushing the technical bounds of experimentation while meeting internal client commitments.
Other responsibilities:
Demonstrate competency in the creation, validation, and application of statistical models as well as in implementing machine learning solutions
Participate on 3-4 projects concurrently
Selecting features, building and optimizing classifiers using machine learning techniques following the governance in place
Demonstrate thorough knowledge of CNH Industrial; its products and services; its internal systems, processes, and procedures; and its relevant external influences (competitors, laws and regulations, market conditions, etc.)
Demonstrate strong client focus in the application of software tools to solve business problems
Extending company’s data with third party sources of information when needed
Processing, cleansing, and verifying the integrity of data used for analysis
Facilitate the creation of a data-driven culture, driving business decisions via insights from data
Identify, compare, and develop technologies to address new enterprise needs, especially around orchestrating machine learning at scale
Oversee the preparation of high-quality project deliverables that are valued by the business and present them in such a manner that they are easily understood by project stakeholders
- Demonstrate a strong focus on continual learning in the Analytics field, with strong initiative to research and apply new methods and digital technologies to be competitive
Below is a rough timeline of where you can expect to be at different points during your career path starting in this position:
Upon joining:
– Meet the rest of the team and start meeting members of teams around the company
– Spending time learning about our cloud data warehouse, our code base, and development process
– Join standing calls with established teams to start learning the ins and outs
– Familiarize yourself with the state of the art in EDA, current metrics, data sets, and benchmarks
– Contribute as needed (find something that doesnt spark joy? Or that is not yet implemented? Just plain wrong? Send a PR!)
Within a month:
– Become deeply familiar with Databricks and Azure ML Studio implementations with real world data and query patterns: finding new data sources, new query patterns, new features
– Developing (or collaborating with developers) improvements to measurement, running, data storage, communicating, and reporting infrastructure
– Develop reproducible reports and especially reporting infrastructure so that we can share our results with our teams and/or all of CNH Industrial
– Collaborating closely with developers working on tooling and the developer establishment to make benchmarks easy to run reliably with interpretable results
– Run and extend reports for collaborators on benchmarks
Within 6 months:
– Writing entirely new benchmarks (and classes of benchmarks) with real-world data and query patterns: finding new data sources, new query patterns, new features
– Developing and collaborating with developers to make improvements to measurement, running, data storage, communication, and reporting infrastructure
– Develop reproducible reports and especially reporting infrastructure so that we can share our results with our teams and/or all of CNH Industrial
– Collaborating closely with developers working on tooling and developer enablement to make benchmarks easy to run reliably with interpretable results
Within 12 months:
– Defining new metrics for performance and optimization
– Explore deeper and more complicated metrics, measures, and benchmarks (either deep diving into how subtle data features impact performance or broader scope: find data sets that stretch current capabilities and find ways to measure and make those performant
– Develop and collaborate on new frontiers of performance (profiling interfaces, automated testing systems, randomized benchmarking)
Required Skills (These are skills that candidates MUST possess)
Excellent understanding of machine learning and ML classification performance measures
Applied statistics skills, such as distributions, statistical testing, regression, classification etc.
Good scripting and programming skills
Ability to communicate effectively about AI/ML with a broad range of audiences, from sophisticated technical collaborators to those with little or no relevant background
Advanced skill in translating business requirements into detailed AI/ML and analytics requirements
Must be comfortable working in industry standard statistics and data visualization software and packages.
Must demonstrate strengths in initiative, innovation, interpersonal skills and people development.
Preferred Skills (These are skills that would be NICE to possess)
The candidates background and prior experience typically requires a bachelors degree, preferably in data science, statistics, economics, mathematics, engineering or a similar field with significant quantitative coursework, with 5-6 years of professional experience in quantitative analysis, or a masters degree with 1-2 years of experience, or a PhD. We are especially interested in people who have domain experience working with data in finance, agriculture, and industry and want to make it better.
Other previous experience that will be helpful (though not every item is a pre-requisite):
– Familiarity with communicating to describe uncertainty and make data-driven decisions in
o Survival/reliability, NLP, GAMs, causal inference, time series, recommenders, anomaly detection, geospatial, graph networks, optimizations
o Data visualization tools (Power BI, Qlik sense, ggplot, matplotlib)
– Familiarity with modern reproducible reporting infrastructure (RMarkdown, PYMarkdown, etc.)
– Experience with data analysis, cleaning, feature engineering, and model preparation in languages like Python, R, etc. along with
o PCA and regularization
o Cross validation and bootstrapping
o Bayesian techniques
o GBMs
– Familiarity with data protection and privacy requirements worldwide
– Familiarity with explain-ability frameworks (Lime, partial dependence plots)
– Experience with data systems (a few examples, familiarity with all is not expected*):
o SQL, dplyr, pandas
o Spark, Databricks, Dask
o Open-source package development and package management
– Experience with team collaboration environments
o Azure DevOps, Github, Confluence, Jira
– Experience with MLOps implementations (Kedro, MLFlow, etc.) and maturity
People who thrive in this role tend to:
– Find joy in working directly with and unblocking internal customers.
– Evangelize data science. Youll spend a lot of time talking to others about data science workflows and use cases.
– Be interested in the infrastructure and architecture that underlies large scale enterprise data science.
– Be hungry and humble. Machine learning is growing quickly into an impossibly broad technical scope. They are quick to admit what they dont know and are constantly seeking to learn more.
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