Remote Data Scientist
Remote Los Angeles Part-time Mid-level Data Science
At a glance
Mid-level Data Science role at Online River. Remote, United States · part-time.
Pay not stated Data Scientist salary
RoleData Science
SeniorityMid-level
LocationLos Angeles
WorkplaceRemote
EmploymentPart-time
PostedSep 28, 2024 · 2y ago
Role brief
dataroles summary, based on the employer's posting.
What you'll do
- Gather, clean and prepare datasets so they are reliable for analysis
- Explore datasets to spot patterns and check whether hypotheses hold up
- Build, implement and tune predictive models and machine learning methods
- Create dashboards and visualizations, then share findings with partner teams
- Document the analytical steps and methods used in your work
What you bring
- A bachelor’s or master’s degree in data science, statistics, computer science or a related field
- Experience analyzing data and building models, plus a solid grasp of machine learning
- Programming skills in Python or R, familiarity with Pandas, NumPy or Scikit-learn, and SQL experience
Who this fits
This part-time remote role suits someone who can work independently and manage several projects. You’ll also need to explain analytical findings clearly in writing and conversation.
From the employer
We are seeking a skilled Data Scientist to join our team remotely. The ideal candidate will leverage statistical analysis, machine learning, and data visualization techniques to extract insights and inform business decisions. This role involves collaborating with cross-functional teams to define analytical requirements and deliver actionable solutions.
Data Collection & Preprocessing:
Statistical Analysis:
Model Development:
Data Visualization:
Collaboration:
Documentation:
Continuous Learning:
Data Collection & Preprocessing:
- Gather, clean, and preprocess datasets to ensure quality and integrity.
Statistical Analysis:
- Conduct exploratory data analysis to identify patterns and validate hypotheses.
Model Development:
- Develop, implement, and optimize predictive models and machine learning algorithms.
Data Visualization:
- Create visualizations and dashboards to communicate insights effectively.
Collaboration:
- Work with cross-functional teams to define project objectives and present findings.
Documentation:
- Maintain thorough documentation of analytical processes and methodologies.
Continuous Learning:
- Stay updated on data science trends and participate in professional development activities.