Analyze large and complex datasets to identify trends, patterns, and insights.
Develop and implement machine learning models for prediction, classification, and clustering tasks using Python libraries like scikit-learn, TensorFlow, or PyTorch.
Perform statistical analysis and hypothesis testing to validate findings and draw meaningful conclusions.
Design and implement data visualization dashboards and reports using Python libraries like Matplotlib, Seaborn, or Plotly to communicate insights effectively.
Collaborate with cross-functional teams to understand business requirements and translate them into data science solutions.
Build and deploy scalable data pipelines using Python and related tools.
Stay up-to-date with the latest advancements in data science, machine learning, and Python libraries.
Communicate complex data insights and findings to both technical and non-technical audiences.
What You'll Bring
Proven experience as a Data Scientist or similar role (ideally 2+ years).
Strong programming skills in Python, with experience in relevant data science libraries (e.g., Pandas, NumPy, Scikit-learn).
Solid understanding of statistical concepts, machine learning algorithms, and data modeling techniques.
Experience with data visualization using Python libraries (e.g., Matplotlib, Seaborn, Plotly).
Ability to work with large datasets and perform data cleaning, preprocessing, and feature engineering.
Strong problem-solving and analytical skills.
Excellent communication and presentation abilities.
Bachelor's or Master's degree in a quantitative field such as Computer Science, Statistics, Mathematics, or a related area.
Experience with big data technologies (e.g., Spark, Hadoop).
Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and their data science services.
Experience with deep learning frameworks (e.g., TensorFlow, PyTorch).
Knowledge of database technologies (SQL and NoSQL).
Experience with deploying machine learning models into production.
(ref:hirist.tech)
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