Data Scientist
Data Scientist
Employer Active
Job Alert
You will be updated with latest job alerts via email
Share
Job Alert
You will be updated with latest job alerts via email
Not Disclosed
Salary Not Disclosed
1 Vacancy
Job Description
-
Problem Identification: Work with business stakeholders (e.g. marketing finance product) to understand business challenges and identify opportunities where data science can provide a solution.
-
Data Collection & Management: Identify collect and organize large complex and sometimes unstructured datasets from various sources (e.g. internal databases APIs web scraping).
-
Data Wrangling and Cleaning: Clean preprocess and transform raw data into a usable format. This is often a time-consuming but critical part of the job to ensure data quality and accuracy.
-
Exploratory Data Analysis (EDA): Perform in-depth analysis of the data to uncover patterns trends and relationships. This involves using statistical methods and data visualization tools.
-
Model Development: Design build train and test machine learning models and algorithms (e.g. for classification regression clustering forecasting).
-
Model Deployment: Work with data engineers and software developers to deploy models into production environments and monitor their performance.
-
Communication and Storytelling: Translate complex technical findings into clear actionable business insights. This often involves creating compelling reports presentations and interactive dashboards for a non-technical audience.
-
Continuous Improvement: Stay up-to-date with emerging data science technologies methods and tools. Continuously refine models and analytical processes to improve efficiency and accuracy.
Technical Skills:
-
Programming Languages: Proficiency in at least one or more data-centric languages such as Python (most common with libraries like NumPy Pandas Scikit-learn TensorFlow PyTorch) and R (popular for statistical analysis).
-
Database Management: Strong knowledge of SQL for querying and managing databases. Experience with NoSQL databases may also be required.
-
Statistics and Mathematics: A solid foundation in statistical concepts including probability hypothesis testing regression analysis and experimental design (e.g. A/B testing).
-
Machine Learning: A deep understanding of machine learning algorithms and techniques including supervised and unsupervised learning and model evaluation metrics.
-
Data Visualization: Experience with data visualization tools like Tableau Power BI Matplotlib Seaborn or to create charts dashboards and reports.
-
Big Data Technologies: Familiarity with big data tools and frameworks like Apache Spark Hadoop and cloud platforms (e.g. AWS Azure) is increasingly important.
Employment Type
Full-time
