Data Scientist

Denver, CO - USA

Monthly Salary: Not Disclosed

Posted on: 30+ days ago

Vacancies: 1 Vacancy

Job Summary

Responsibilities

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.

Required Skills and Qualifications

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.

Responsibilities 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 somet...

Responsibilities

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.

Required Skills and Qualifications

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.