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Data Scientist

Flexon Technologies, INC.
Pleasanton, CA Full Time
POSTED ON 1/14/2025 CLOSED ON 4/7/2025

What are the responsibilities and job description for the Data Scientist position at Flexon Technologies, INC.?

Job Description

Job Summary :

We are seeking a talented and motivated Junior Data Scientist to join our data analytics team. As a Junior Data Scientist, you will work on various data projects, assisting in data analysis, modeling, and developing machine learning solutions. You will collaborate with senior data scientists and cross-functional teams to extract insights from data and contribute to data-driven decision-making. The ideal candidate should have a strong foundation in data science concepts, programming skills, and a passion for solving complex problems using data.

Qualifications :

  • Bachelor's or Master's degree in Data Science, Computer Science, Statistics, or a related field. (Graduated in 2024 or before)
  • Strong understanding of data science concepts, statistical analysis, and machine learning algorithms with 1 to 2 years of experience.
  • Proficiency in programming languages such as Python or R for data manipulation, analysis, and modeling (1 to 2 years of experience).
  • Familiarity with data visualization tools and libraries, such as Matplotlib or Tableau.
  • Experience with data preprocessing, feature engineering, and data cleaning techniques.
  • Basic knowledge of SQL for data querying and manipulation.
  • Strong problem-solving skills and ability to think analytically.
  • Excellent communication skills, with the ability to effectively present complex concepts and findings to non-technical stakeholders.
  • Ability to work collaboratively in a team environment and contribute to team goals.
  • Knowledge of cloud platforms like AWS or Azure and experience working with big data technologies are desirable.
  • Familiarity with version control systems like Git and knowledge of software development practices is a plus.
  • Basic understanding of Machine Learning Operations (MLOps) : Familiarity with the lifecycle of machine learning models from development to deployment and maintenance.
  • Knowledge of Data Ethics and Privacy : Understanding of data privacy laws (like GDPR) and ethical considerations in data science.
  • Adaptability to New Technologies : Ability to quickly learn and adapt to new data analysis tools and techniques as they emerge.
  • Critical Thinking : Ability to approach problems critically and propose innovative solutions.
  • Project Management Skills : Basic project management skills to manage tasks efficiently and meet deadlines.

Responsibilities :

  • Assist in data collection, preprocessing, and cleaning to ensure data quality and usability for analysis.
  • Perform exploratory data analysis to identify patterns, trends, and relationships in the data.
  • Develop and implement statistical and machine learning models to solve business problems.
  • Collaborate with senior data scientists to design and execute experiments, analyze results, and generate actionable insights.
  • Conduct data visualization to effectively communicate findings and present insights to stakeholders.
  • Assist in developing and maintaining data pipelines and workflows for data ingestion, transformation, and analysis.
  • Stay abreast of the latest data science techniques, tools, and methodologies, and apply them to enhance analytics capabilities.
  • Collaborate with data engineering and IT teams to ensure seamless integration and data availability for analysis.
  • Contribute to developing and improving data science workflows and best practices.
  • Document and communicate the methodology, assumptions, and limitations of data models and analyses.
  • Assist in model deployment : Aid in deploying machine learning models into production environments, ensuring they run efficiently and reliably.
  • Participate in code reviews : Engage in code review processes to ensure quality and adherence to best practices.
  • Data quality assurance : Regularly check data sources for integrity and accuracy.
  • Continuous learning : Commitment to continuous learning and staying updated with advancements in data science and related fields.

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