Junior data scientist, machine learning

DESCRIPTION
Developing with a skilled team automations that were implemented in SAS/SQL scripts and troubleshooting in several business areas for an insurance company.

SOFTWARE
SAS (Guide), PL/SQL, Oracle, Serena Dimensions, Excel.

KEY RESPONSIBILITIES
- Automation and performance tuning implemented in scripts by collaboration with users and team leaders by regular meetings.
- Resolution of different kind of issues in daily, weekly and monthly SAS scripts in BI department in a fast-paced environment.

ACHIEVEMENTS
- Ambitious automation project based on SAS and SQL scripts reached in only 5 months.
- Significant improvements in the documentation of several areas.

DESCRIPTION
Maintaining, improving and supporting tasks on the scripts that collect customer information for 2 Balanced Scorecards (BSC) for an energy utilities company (Naturgy).

SOFTWARE
SAS (Base, Guide), PL/SQL, Oracle, Tableau, Excel.

KEY RESPONSIBILITIES
- Responsible of the SAS and SQL code and documentation of aforementioned BSC (one for companies and other for private individuals).
- Collaborating with BIMA (BI and Marketing Analytics) department through suggestions, improvements and new applications for BSC output data.
- Knowledge transfer of these BSC (data sources, potential issues, suggestions…etc) to other members of the department with the purpose of generating value from them.

ACHIEVEMENTS
- A BSC for a huge amount of data (about 4.5 million customers and 11 million contracts) was successfully developed 2 months before the given deadline.
- Creation of a useful product transition matrix to quantify and visualise customers profit and its changes.

DESCRIPTION
Control and identification of issues through BAU (Business as Usual) analysis related to DQRS area (Data Quality Risk Systems) for a well-known banking client (Bank of Santander). 

SOFTWARE
SAS (Base), PL/SQL, Oracle, Excel, Access.

KEY RESPONSIBILITIES
- Checking integrity and reliability of data by obtaining and analysing several data quality KPI.
- Detection and reporting of significant daily financial deviations.

ACHIEVEMENTS
- Great time reduction in monthly in and daily tests due to a better organisation and code cleaning.

Diploma consistente en 45 créditos ECTS con un alto contenido práctico acerca de las tecnologías, herramientas y casos prácticos relativos a aspectos clave del IoT tales como Data Science, Big Data, Industria 4.0, Smart Cities...etc.

ASIGNATURAS:

Bloque I - Internet de las Cosas y Contexto de Smart Cities
Bloque II - Plataformas e Integración de Sistemas y Herramientas
Bloque III - Ciencia de datos
Bloque IV - Aplicaciones IoT
Bloque V - Aspectos Legales y Seguridad
Bloque VI - Dispositivos para IOT


Tecnologías y herramientas software
- R
- Spark (Apache Spark, SparkR)
- NoSQL: CartoDB, ArangoDB, MongoDB
- Plataformas IoT: AWS IoT, Thingsworx
- Smart Cities
- SoC (Raspberry Pi)
- Industria 4.0

60 Créditos ETCS, programa de 1 año.

CONTENIDO:

- Técnicas de minería de datos: Regresiones, redes neuronales, técnicas basadas en árboles, segmentaciones, análisis multivariantes, ARIMA, técnicas de remuestreo...

- Inteligencia de Negocios: CRM, modelos de decisión de Marketing, gestión de la satisfacción, cuadro de mando integral...

- Minería de textos: Análisis de textos, lenguaje natural, análisis de sentimientos...

- Depuración, gestión y mejora de Bases de Datos.


SOFTWARE:

SAS, R, SQL, Knime, Vtiger.

Distinción en Economía y Métodos Cuantitativos 

Matrículas de Honor:

- Complementos de Macro y Microeconomía
     Uso de Stata para ciencia del deporte

- Técnicas Estadísticas Aplicadas a la Empresa
     Uso de R para técnicas predictivas y métodos cuantitativos