Data Miner

Angajator: APT Resources & Services SRL
Domeniu:
  • Inginerie
  • IT Hardware
  • IT Software
  • Tip job: full-time
    Nivel job: 1 - 5 ani experienta
    Orase:
  • BUCURESTI
  • Actualizat la: 20.11.2017
    Scurta descriere a companiei

    APT RESOURCES & SERVICES SRL

    Cu o prezență de peste 20 de ani pe piața românească de resurse umane, grupul de firme APT este cel mai mare jucător independent din industrie, o concentrare de forțe repartizate armonios pe diferite specializări în aria capitalului uman, a comunicării și a serviciilor SaaS. Cifra de afaceri de peste 24 de milioane euro în 2015 și o medie de 2000 de persoane (majoritatea cu studii superioare), angajate în servicii BPO și misiuni de muncă temporară, au menținut grupul de firme APT în avangarda primilor cinci furnizori de servicii complexe de HR din România.

    Strategia Grupului APT se concentrează pe dezvoltarea proactivă a pieței muncii, pentru care identifică, pregătește și oferă specialiști competenți, absolvenți sau studenți cu potențial. Noi aducem omul potrivit la locul potrivit. Cu un palmares de peste 10.000 de angajări, acoperim cele mai căutate domenii, cu focus pe IT&C, BPO, finanțe-bănci, inginerie, retail, medical & pharma. Am dezvoltat instrumente multidisciplinare de lucru și avem soluții de recrutare rapide și inovatoare, susținute și prin platforma online aptjob.ro. Începând cu 2015, grupul și-a extins operațiunile și în alte două regiuni emergente în piața de HR: Cluj și Brașov, unde funcționează noile sucursale APT.

    Cerinte

    • University studies: Preferably Informatics, Cybernetics, Mathematics, Statistics;
    • Experience> 2 years in reporting / business analysis (preferably in the telecom industry or in data mining & complex statistical analysis);
    • Extensive knowledge of statistical analysis applicable to direct marketing activities (customer contact and product, offer and service);
    • Clear understanding of analysis, reporting and evaluation systems (eg SAS, Enterprise Guide, Business Objects, SQL) as well as data and technical requirements require support for these applications.
    • Solid MS Office (Excel - Advanced, Access, Word, PowerPoint).
    • Deep understanding of data mining and segmentation techniques: decision trees, logistic regression, neural networks, hierarchical clustering, partitioning clustering.

    Responsabilitati

    • Providing support for business decisions and implementation of Customer Value Management strategy by delivering analyzes and insights (models of propensity, segmentation, complex analyzes, profiles, analyzes of direct marketing actions).
    • Identifying business opportunities together with senior team members and product managers based on collected insight.
    • Developing detailed analyzes of business topics to identify their causes, motives and meaning.
    • Developing customer base and direct marketing campaigns (campaigns, promotions, offers) to understand customer behavior and business impact.
    • Providing recommendations based on insights from analyzes, proactively identifies opportunities to improve / initiate direct marketing actions.
    • Assisting the data mining manager in developing the structure / analytical framework and strategies to achieve business goals
    • Developing up-sell, cross-sell, acquisition, retention, cost-to-mobile and profitability models by identifying, building and implementing analytical projects using advanced statistics and modeling techniques.
    • Cross-functional project: is responsible for managing the implementation and monitoring of the Universal Control Group and for proposing the necessary actions to ensure its quality.
    • Collects and integrates qualitative and quantitative feedback into analysis development.
    • Define the objectives and requirements for data extraction and analytical aggregates.
    • Develop ad-hoc analysis and analytical reports to support the needs of Base Management.
    • Identify areas where statistical analysis & modeling can add value.
    • Ensure re-use of scriptures / procedures developed to allow faster provisioning of information.
    • Develop, document, and facilitate the transfer of scoring scraps to data specialists.
    • Measures the performance of propensity and segmentation patterns.