Data Scientist - Python/Tensorflow

As a Data Scientist, you will be responsible for analyzing large and complex datasets to identify trends, patterns, and insights. Your role will involve developing and implementing machine learning models for prediction, classification, and clustering tasks using Python libraries such as scikit-learn, TensorFlow, or PyTorch. You will also conduct statistical analysis and hypothesis testing to validate findings and draw meaningful conclusions. In this position, you will design and implement data visualization dashboards and reports using Python libraries like Matplotlib, Seaborn, or Plotly to effectively communicate insights. Collaboration with cross-functional teams to understand business requirements and translate them into data science solutions will be a key aspect of your responsibilities. Additionally, you will build and deploy scalable data pipelines using Python and related tools. To excel in this role, you should bring proven experience as a Data Scientist or in a similar role, ideally with 2+ years of experience. Strong programming skills in Python and familiarity with relevant data science libraries such as Pandas, NumPy, and Scikit-learn are essential. A solid understanding of statistical concepts, machine learning algorithms, and data modeling techniques is required. You will need experience in data visualization using Python libraries like Matplotlib, Seaborn, and Plotly. The ability to work with large datasets, perform data cleaning, preprocessing, and feature engineering is crucial. Strong problem-solving and analytical skills, along with excellent communication and presentation abilities, will be valuable assets in this role. A Bachelor's or Master's degree in a quantitative field such as Computer Science, Statistics, Mathematics, or a related area is preferred. Experience with big data technologies like Spark and Hadoop, familiarity with cloud platforms such as AWS, Azure, and GCP, and knowledge of database technologies (SQL and NoSQL) are advantageous. Experience with deep learning frameworks like TensorFlow and PyTorch, as well as deploying machine learning models into production, will also be beneficial for this position. Stay up-to-date with the latest advancements in data science, machine learning, and Python libraries to contribute effectively to the team. (ref:hirist.tech),