Machine Learning Developer - Data Science

Machine Learning Developer

We are seeking a highly skilled and experienced Machine Learning Developer to join our global service-based client on a full-time basis. 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 :

- 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.

Required Skills and Experience :

- 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.

Good to Have :

- Exposure to cloud platforms like AWS, Azure, or GCP.

- Knowledge of ML model deployment and monitoring techniques.

- Understanding of MLOps workflows