
Data Driven Solutions Specialist
2 weeks ago
We are seeking a highly skilled and experienced Senior Data Scientist to join our team. The ideal candidate should have a strong foundation in Python programming and be well-versed in applying machine learning models, especially in the areas of time series forecasting and regression.
This role offers the opportunity to work on complex data-driven solutions and contribute to impactful business insights and automation initiatives.
Key Responsibilities:- Design, develop, and deploy machine learning models and data-driven solutions for real-world business problems.
- Work extensively on time series forecasting, regression models, and supervised/unsupervised learning techniques.
- Develop and maintain robust, scalable, and reusable code using Python and relevant ML libraries.
- Analyze large datasets, extract meaningful patterns, and engineer relevant features.
- Collaborate with data engineers, business stakeholders, and software developers to integrate ML models into production systems.
- Optimize model performance and regularly tune hyperparameters to improve accuracy and efficiency.
- Create comprehensive documentation and visualizations using tools like matplotlib or seaborn.
- Stay updated with the latest research, tools, and trends in machine learning and data science.
- Minimum 8+ years of total experience, with 5+ years in Machine Learning or Data Science roles.
- Strong proficiency in Python programming, scripting, and data manipulation.
- In-depth experience with key libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, etc.
- Solid hands-on expertise in Time Series Forecasting, including techniques like ARIMA, Prophet, LSTM, or other advanced models.
- Strong foundation in regression analysis, linear and non-linear models.
- Good understanding of statistics, probability, and data structures.
- Ability to write clean, modular, and efficient code suitable for deployment.
- Familiarity with Jupyter Notebooks, Git, and standard development practices.
- Experience with deep learning frameworks such as TensorFlow or PyTorch.
- Exposure to cloud platforms like AWS, Azure, or GCP.
- Knowledge of ML model deployment and monitoring techniques.
- Understanding of MLOps workflows
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