Data Scientist at KesarWeb
Flint, MI 48505, USA -
Full Time


Start Date

Immediate

Expiry Date

20 May, 25

Salary

0.0

Posted On

21 Feb, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Soft Skills, Communication Skills

Industry

Information Technology/IT

Description

JOB SUMMARY:

We are seeking a talented and innovative Data Scientist to drive data-driven decision-making and deliver actionable insights through advanced analytics and machine learning. You will work with large datasets, develop predictive models, and collaborate with cross-functional teams to create impactful solutions that align with business goals.

SOFT SKILLS:

  • Excellent problem-solving and critical-thinking abilities.
  • Strong communication skills to convey complex data concepts in a simple manner.
  • Ability to work independently and collaboratively in a team environment.
  • Attention to detail and commitment to delivering high-quality work.
Responsibilities
  • Data Exploration and Analysis:
  • Collect, clean, and preprocess data from various sources for analysis.
  • Conduct exploratory data analysis (EDA) to uncover trends, patterns, and anomalies.
  • Model Development:
  • Design and implement machine learning models for classification, regression, clustering, and other tasks.
  • Develop and validate predictive models to support business strategies.
  • Data Visualization and Communication:
  • Create clear and compelling visualizations to communicate findings using tools like Tableau, Power BI, or Python libraries (e.g., Matplotlib, Seaborn).
  • Present insights and recommendations to technical and non-technical stakeholders.
  • Research and Innovation:
  • Stay updated with the latest advancements in data science, AI, and machine learning.
  • Experiment with new algorithms and methodologies to improve model performance.
  • Collaboration:
  • Work closely with data engineers to build and maintain data pipelines and infrastructure.
  • Collaborate with business units to understand their objectives and translate them into data-driven solutions.
  • Deployment and Monitoring:
  • Deploy machine learning models into production environments.
  • Monitor and evaluate model performance, retraining as necessary to ensure accuracy and relevance.
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