Junior Data Scientist

at  NIELSENIQ

OUR BENEFITS

• Flexible working environment
• Volunteer time off
• LinkedIn Learning
• Employee-Assistance-Program (EAP)
  Job Description
  CGA by Nielsen IQ are looking for an enthusiastic, proactive problem-solver, with excellent communication skills, and a passion for the hospitality market, to join our growing Data Science team based in our Stockport office.
  As a Data Scientist, you’ll join a dynamic team dedicated to developing innovative solutions that transform vast amounts of raw data into actionable insights, drive decision-making, and develop new methodologies to enhance our products and services. The successful candidate will join a dynamic team with a broad remit across diverse data sets and modeling techniques, where expertise in Natural Language Processing (NLP) would be an advantage.
  Qualifications

QUALIFICATIONS AND SKILLS

• Educational Background: Master’s or Ph.D. in Computer Science, Statistics, Applied Mathematics, or a related field (or equivalent experience).
• Programming Proficiency: Proficiency in Python for data science applications (e.g., data wrangling, modelling, web-scraping). Familiarity with SQL for database querying.
• Machine Learning Libraries: Skilled in using libraries such as Scikit-learn, TensorFlow, Keras, and PyTorch.
• Data Manipulation: Experience with libraries like Pandas or NumPy.
• Data Visualization: Proficiency with Matplotlib, Seaborn, or Plotly for visualising complex datasets.
• Databases: Experience with relational databases (e.g., PostgreSQL, MySQL). Knowledge of non-relational databases (e.g., MongoDB) is an additional benefit.
• Big Data Technologies: Familiarity with platforms like Hadoop, Spark, or similar technologies for processing large datasets.
• Communication and Collaboration: Strong communication, writing, and collaboration skills.
  Additional Information

• Data Management: Extract, preprocess, and clean data from multiple sources to create unified datasets.
• Exploratory Data Analysis: Perform exploratory data analysis (EDA) to identify trends, anomalies, and patterns.
• Model Development: Design, implement, and validate predictive and prescriptive models using machine learning algorithms.
• Collaboration: Work with cross-functional teams, understanding their needs, and translating them into data-driven solutions.
• Communication and Presentation Skills: Present findings, strategies, and recommendations to technical and non-technical audiences throughout the development process. Excellent communication skills, both verbal and written.
• Model Deployment: Assist in the production deployment of proposed enhancements and new solutions.
• Model Evaluation: Develop testing and validation strategies to measure the effectiveness of new models, in coordination with other analytics teams.