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Sr. Manager of Data Analytics

GOcxm is a company focused on providing digital services that help our clients connect and engage their consumers. Through its proprietary platform GOcxm provides easy, low friction solutions that clients can easily integrate into their plans and programs. One of GOcxm’s strongest value propositions is its ability to capture data, analyze it and provide it back to the client as insights and action plans.

As a Sr. Manager of data analytics, you will be critical to collecting, analyzing and delivering insights to our clients. This is a strategically critical role to delivering our value proposition.

Job Description

Your role will be to play a leadership role in the business by bringing a data and analytics perspective to all major business and client discussions. This will involve both an internal focus on process improvement, automation, building scalable solutions as well as externally finding new and interesting ways to deliver value through data to our clients. Your role will be analytically focused and require activities that include dashboards and reporting, ad hoc data related business or client challenges, and special projects

Primary areas of responsibility

Collect, organize and analyze large sets of data to discover patterns
Ensure completeness, accuracy and quality of data received
Data mining and data auditing to identify sources of client value
Identify anomalies in the data and work with dev team and client to resolve them
Oversee the creation and development of dashboards to track and optimize client performance (Google Data Studio skills an asset)
Accurately identify new and innovative data sets that can be used to improve existing measurement models and brainstorm new ways to utilize the data to build value for clients and GOcxm
Design, configure and tune the ML/AI product models to generate the necessary outputs
Analyze and resolve any ongoing issues with model performance
Work closely with Product development to ensure Data requirements are satisfied, optimized and built into all new products and product upgrades
Creation of case studies to demonstrate power and value of analytics to new clients
Apply domain knowledge from clients to extract features from the input data

Required skills and competencies
Analysis:

High level data expertise: expected to proficient in all aspects of data analysis, including structure, retrieval, wrangling, analytics, and visualization
Tracking and metrics: lead the development (at a client level) and calculation / visualization of results
Ability to solve routine problems and turn them into standard approaches that can be leveraged with clients and within account management
Ability to tackle undefined problems into routine processes to ensure productivity and capability building

Communication:

Ability to deliver strategic presentations that involve high degrees of complexity to drive strategy and investment decisions
Ability to distill complex data and technical subject matter into a format that can be understood and consumed by clients and internal account management team
Ability to build compelling stories to provide direction to clients and account management team
Change management familiarity: co-develop a change management plan that includes clear plans and dates for communication and training across all of the key stakeholder groups
Ability to train colleagues in the use and interpretation of data and insights

Technical requirements:

Architecture and technical data design oversight: create and lead the technical blueprint that will guide the technical team’s efforts
Ability to access disparate data sources and reconstruct them in a way that they can be manipulated and analyzed
Ability to query, manipulate and analyze data using R, SQL, Big Query, and Python
Ability to visualize data using Google data studio, Excel, Tableau or MS Power BI
Understanding of descriptive, predictive and prescriptive data techniques that involve ML and / or AI
Understanding of regression and / or time series / classification / supervised and unsupervised modeling
Expertise in building and evaluating model output then identifying model improvement opportunities through testing and quality control
Data security: understand and adhere to information security policies and guidelines; practice them for protection of organizational data and information system
Governance and data transparency: play a leading role in establishing the right data governance principles and ensuring they are complied with