Job Detail

Minimum Degree Required:
Bachelor Degree
Additional Educational Requirements:
In lieu of a Bachelor Degree, 15 years of professional experience involving technology-focused process improvements,
transformations, and/or system implementations.
Minimum Years of Experience:
13 year(s)
Preferred Qualifications:
Degree Preferred:
Master Degree
Preferred Fields of Study:
Analytics, Artificial Intelligence and Robotics, Business Analytics, Computer and Information Science, Computer
Engineering & Accounting, Management Information Systems, Mathematics
Certification(s) Preferred:
PMI Certified, Agile Certified Scrum Master
Preferred Knowledge/Skills:
Proven Track record of leading, coaching and developing data science and ML technology teams.
Demonstrates extensive knowledge and/or a proven record of success in the following areas:
Understanding of NoSQL (Graph, Document, Columnar) database models, XML, relational and other database
models and associated SQL;
Understanding of ETL tools and techniques, such as tools like Talent, Mapforce, how to map transformation and flow
of data from a source to a target system;
Performing in development language environments: e.g. Python, Java, Scala, C++, R, SQL, etc. and applying
analytical methods to large and complex datasets leveraging one of those languages;
Applying statistical modelling, algorithms, data mining and machine learning algorithms problem solving;
Managing business development such as client relationship management and leading and contributing to client
proposals;
Delivering and tracking successfully large-scale projects, including ownership of architecture solutions and managing
change;
Leading, training and working with other data scientists in designing effective analytical approaches taking into
consideration performance and scalability to large datasets;
Manipulating and analyzing complex, high-volume, high-dimensionality data from varying sources;
Demonstrating proven ability with NLP and text based extraction techniques;
Developing data science analytic models and simultaneously operationalizing these models so they can run in an
automated context; and,
Understanding of machine learning algorithms, such as k-NN, GBM, Neural Networks Naive Bayes, SVM, and
Decision Forests. Demonstrates extensive abilities/proven record of success in the application of statistical or
numerical methods, data mining or data-driven problem solving, including the following areas:
Understanding of NoSQL (Graph, Document, Columnar) database models, XML, relational and other database
models and associated SQL;
Understanding of ETL tools and techniques, such as tools like Talent, Mapforce, how to map transformation and flow
of data from a source to a target system;
Performing in development language environments: e.g. Python, Java, Scala, C++, R, SQL, etc. and applying
analytical methods to large and complex datasets leveraging one of those languages;
Applying statistical modelling, algorithms, data mining and machine learning algorithms problem solving;

Managing business development such as client relationship management and leading and contributing to client
proposals;
Delivering and tracking successfully large-scale projects, including ownership of architecture solutions and managing
change;
Leading, training and working with other data scientists in designing effective analytical approaches taking into
consideration performance and scalability to large datasets;
Manipulating and analyzing complex, high-volume, high-dimensionality data from varying sources;
Demonstrating proven ability with NLP and text based extraction techniques;
Developing data science analytic models and simultaneously operationalizing these models so they can run in an
automated context; and,
Understanding of machine learning algorithms, such as k-NN, GBM, Neural Networks Naive Bayes, SVM, and
Decision Forests.
Utilizing and applying knowledge commonly used data science packages including Spark, Pandas, SciPy, and Numpy;
Demonstrating familiarity with thorough learning architectures used for text analysis, computer vision and signal
processing;
Utilizing programming skills and knowledge on how to write models which can be directly used in production as part
of a large scale system;
Utilizing and applying knowledge of technologies such as H20.ai, Google Machine Learning and Deep learning;
Applying techniques such as multivariate regressions, Bayesian probabilities, clustering algorithms, machine
learning, dynamic programming, stochastic-processes, queuing theory, algorithmic knowledge to efficiently research
and solve complex development problems and application of engineering methods to define, predict and evaluate the
results obtained;
Developing end to end deep learning solutions for structured and unstructured data problems;
Developing and deploying AI solutions as part of a larger automation pipeline;
Utilizing programming skills and knowledge on how to write models which can be directly used in production as part
of a large scale system;
Using common cloud computing platforms including AWS and GCP in addition to their respective utilities for
managing and manipulating large data sources, model, development, and deployment; and,
Visualizing and communicating analytical results, using technologies such as HTML, JavaScript, D3, Tableau, and
PowerBI.