Overview
On Site
140000
Full Time
Skills
Statistics and Mathematics
Python
Data Analysis
Job Details
Position: Data Scientist
Location: Denver, CO (must work in the
office)
Key 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 D3.js 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.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.