Job Responsibilities :

- Data mining using state-of-the-art methods

- Enhancing data collection procedures to include information that is relevant for building analytic systems

- Processing, cleansing, and verifying the integrity of data used for analysis

- Doing ad-hoc analysis and presenting results in a clear manner

- Creating automated anomaly detection systems and constant tracking of its performance

- Selecting features, building and optimizing classifiers using machine learning techniques

- Responsible for defining and documenting architecture, capturing and documenting non-functional (architectural) requirements, preparing estimates and defining technical solutions to proposals (RFPs)

- Provide technical leadership to project team to perform design to deployment related activities, provide guidance, perform reviews, prevent and resolve technical issues

- Analyses complex business and competitive issues and discerns the implications for systems support.

- Identifies, defines, directs and preforms analysis of technical and economic feasibility of proposed data solution.

- Creates and manages a machine learning pipeline, from raw data acquisitions to merging and normalizing to sophisticated feature engineering development to model execution.

- Designs, leads and actively engages in projects with broad implication for the business and/or the future architecture, successfully addressing cross-technology and cross-platform issues

- Selects tools and methodologies for projects and negotiates terms and conditions with vendors.

- Communicates highly technical information to numerous audiences, including senior management, the user community, and less-experiences staff.

- Leads the organizations planning for the future of the data model and data architecture.

- Exhibit advanced visualization skills, as well as creative problem-solving

- Interacting with customers to have an in-depth understanding of their operations to improve their processes for managing equipment and interfacing.

- Reviewing and providing counsel for all major "Business Case Analysis" effort.

- Developing the interdependencies between major applications/efforts

- Providing project leadership, advice, and counsel to developers, management, customers and project teams on the most complex aspects of application development and system integration.

Basic (Required) Qualifications :

- A Bachelor- s Degree from an accredited college or university or equivalent years (4 years) of experience relevant to this role.

- Seven or more years of progressively responsible role relevant experiences that demonstrate both breadth of business knowledge and depth of digital and analytical skillsets.

- Curiosity about and a deep interest in how digital technology and systems are powering the way users do their jobs.

- Comfortable working in a dynamic environment where digital is still evolving as a core offering.

- Ability to clearly and succinctly explain complex topics.

- Expert in Python; familiar with concurrency in Python

- Track record of architecting and delivering highly available back end systems for large-scale consumer experiences

- Proficient with git or similar source control system; and Experienced with git-based development workflows

- Ability to design and consume well-tailored REST APIs

- Experience working in development teams with code reviews and varying levels of seniority

- Thrives in agile environments but also understands the balance in coding discipline required for large scale, high quality end consumer apps

- Experience both in jumping in to an existing architecture, and starting projects from scratch

- Proven ability to take initiative and dive in to areas of new technology

- Self-motivated with a passion for learning, analysing technology trade-offs, and shipping product

- Good applied statistics skills, such as distributions, statistical testing, regression, etc.

- Proficiency in using query languages such as SQL

- Good scripting and programming skills

- Data-oriented personality

Desired Qualifications :

- Experience with common data science toolkits, such as Python, R, Weka, Matlab, etc (Excellence in at least one of these is highly desirable).

- Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc.

- Experience with data visualisation tools, such as Tableau, Power BI, D3.js, GGplot, etc.

- Experience with NoSQL databases, such as MongoDB, Cassandra, HBase