Data Scientist

Company Name: OSN Employment Type: Full Time Monthly Salary: Unspecified Minimum Work Experience: 5-10 Years Minimum Education Level: Bachelors Degree Listed By: Employer Career Level: Mid-level Description: Description Job Purpose The purpose of this role is to create, score, monitor and effectively utilize advanced analytics models such as predictive models and segmentations, to support the other three pillars of Customers Marketing: Experience, Revenue and Retention. The role will also be responsible of the target list creation for various campaigns by utilizing the outputs of the models (i.e. predictive models scores, segments, recommendation, offer stretch, etc.) Other than the models, this role will also be responsible for transforming data into insights, delivering business analysis, pre and post campaign analysis, in order to effectively support the other three pillars. In addition, role would entail working with the other customer marketing members to support data driven decision making. This role would entail effective model and business analytics handling to support the area to communicate business insight, drive decision making, and understand/validate requirements. Key Accountabilities Planning & Organisation Take full ownership for coordination of the analytical activities, identify technical & organizational problems/issues, develop solutions and fully implement these solutions with agreement from the management where appropriate. Coordinate and track all analytical method development and validation activities according to the requests of the customer marketing team. Financial Excellence (budgets, revenue/profit and costs) Support in the forecast of initiatives within Customer Marketing Provide results and ROI for initiatives within Customer Marketing Customer Excellence (internal/external customer engagement and relationship management) Identify analytical datasets and advanced analysis methodologies using subject matter knowledge in both statistics/mathematics as well as business economics to propose and execute decision making models critical to achieving customer marketing targets Maintain & develop relationships with key individuals within the customer marketing, acting as the ‘focal point'. Nurture partnership with the business units to become an inextricable part of the customer's performance, find opportunities to drive innovation, create new value, and gain recommendations from the customer for new business. Enabling customer marketing to make better, faster decisions based on hard facts and a holistic view of the operation. Drive development of predictive models (e.g. Churn Prediction model, Next Best offer model, Market Basket analysis, Up Sell, Cross Sell models) to leverage existing information assets for optimal Marketing activity. Drive development of descriptive models (e.g. behavioural segmentation) to enhance customer insights. Capitalize opportunities for revenue enhancement through targeted campaigns by development of: Drive Data Mining and Modelling, behavioural analytics, segmentation and advanced analytic across multiple customer and product dimensions including delivery of tangible analytics capabilities in: Produce rule or clustering driven segmentations. Create target lists to support the three pillars of Customer Marketing: Experience, Revenue and Retention Utilize models to optimize the campaign efforts by reducing the cost and/or increasing the reach rate and conversion rate. Productionize the models, monitor and report their performance. Plan scheduled updates, revisions on the models to maintain accuracy. Track and measure performance of initiatives within Customer Marketing Effectively work with BI and IT to have a steady and quality flow of data - Behavioural analytics, measurement and modelling - Customer and Audience segmentation, clustering and profiling - Geo/demographic attribution and segmentation - Audience targeting, A/B and multivariate testing and measurement ▪ Segmentation ▪ Churn modelling ▪ Fraud detection ▪ Behaviour based segmentation ▪ Response modelling ▪ Up-sell/X-Sell modelling ▪ Recommendation engines based on association rules Requirements Qualifications, experience, skills and competencies Experience required: 5+ years of relevant experience in Analytics/BI domain Bachelor's degree in industrial engineering, data mining, statistics, management information systems or equivalent Proven track record of solving challenging problems by applying Advanced Analytics Expert analytical thinking and thought leadership (i.e. being able to develop hypothesis and test them through experiments or analysis) Experience with large data sets Ability to communicate analytical findings to senior management Proven Experience in designing end to end analytical deliverables (sourcing the data from the datawarehouse, creating customer datamart, effectively constructing the analysis or modeling scope, building and scoring the models, automation of the data preparation, scoring and monitoring of the models, creating presentations/ dashboards to effectively explain the model/analysis outputs to different levels such as marketing managers, directors, VPs and CXOs) Education requirement: Knowledge & Skills: Knowledge of SQL in order to analyze and extract customer data from the Enterprise Datawarehouse Knowledge of using BI tools (i.e. Microstrategy, Business Objects, Tableau, Cognos or similar) Knowledge of dashboarding tools such as Qlikview or similar. Advanced statistics and modelling proficiency using SAS EG, SAS Miner, SPSS or similar. Advanced data manipulation techniques using SAS Enterprise Guide or SQL Ability to automate data preparation and scoring by using SAS macros. Ability to create customer level datamarts for analysis and modelling exercises. Ability to check for data quality, create data quality measures and controls to ensure the quality of the data received from BI Strong background in Microsoft Powerpoint and Excel Have a good understanding of business processes Ability to work in a dynamic environment Ability to quickly demonstrate the analytics value by creating prototypes, illustrations, sample cases Able to work autonomously with minimal supervision