Data Scientist

10 hours ago

Nashik, India CareerUS Solutions Full time

Job Summary:

We are seeking a highly skilled and analytical Data Scientist to join our growing team. You will be responsible for extracting insights from large and complex datasets, building predictive models, and supporting data-driven decision-making across the organization. The ideal candidate is passionate about data, has strong statistical and programming skills, and is eager to apply machine learning techniques to real-world problems.

Key Responsibilities:

  • Analyze large datasets to uncover trends, patterns, and insights that inform business decisions.
  • Design and build predictive models and machine learning algorithms.
  • Work closely with stakeholders to understand business challenges and translate them into data-driven solutions.
  • Develop dashboards, reports, and data visualizations to communicate findings clearly.
  • Validate models and conduct A/B testing to measure impact.
  • Collaborate with data engineers to deploy models into production environments.
  • Stay up-to-date with the latest data science tools, techniques, and best practices.

Required Qualifications:

  • Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Data Science, or a related field.
  • Proven experience as a Data Scientist or similar role.
  • Strong programming skills in Python or R.
  • Experience with machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
  • Solid understanding of statistical methods and data modeling techniques.
  • Proficiency in SQL and working with relational and non-relational databases.
  • Experience with data visualization tools like Tableau, Power BI, or matplotlib/seaborn.
  • Excellent problem-solving and communication skills.

Preferred Qualifications:

  • Experience with big data tools (e.g., Spark, Hadoop).
  • Knowledge of cloud platforms (AWS, GCP, Azure).
  • Experience in natural language processing (NLP), time series forecasting, or deep learning.
  • Familiarity with data versioning and MLOps practices.

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