Job Overview:

We are seeking a skilled Python Developer with expertise in Machine Learning (ML) and Data Analytics to join our dynamic team. In this role, you will develop and deploy machine learning models, analyze complex datasets, and drive data-driven insights that help the organization make strategic decisions. The ideal candidate will have a strong background in Python programming, data science, and machine learning frameworks, along with a passion for solving real-world problems through data analysis and predictive modeling.

Key Responsibilities:

Machine Learning Development:

Design, develop, and deploy machine learning models to address business challenges and provide predictive insights.

Implement supervised, unsupervised, and reinforcement learning algorithms based on business requirements.

Perform model tuning, optimization, and validation to ensure high performance and accuracy.

Work with large datasets, ensuring data quality, and applying appropriate preprocessing techniques.

Data Analytics & Visualization:

Analyze large volumes of structured and unstructured data to uncover patterns, trends, and insights.

Develop dashboards and visualizations to communicate findings to non-technical stakeholders.

Perform exploratory data analysis (EDA) to inform business decision-making.

Use statistical methods to validate hypotheses and draw meaningful conclusions.

Collaboration with Stakeholders:

Work closely with cross-functional teams (data engineers, analysts, business teams) to understand business requirements and translate them into actionable ML models and data analysis tasks.

Communicate complex technical concepts and results clearly to business stakeholders.

Data Infrastructure & Tools:

Collaborate with data engineers to build and maintain scalable data pipelines for data collection, transformation, and storage.

Integrate machine learning models into production environments and ensure their continuous monitoring and improvement.

Utilize big data technologies (e.g., Hadoop, Spark) and cloud platforms (AWS, GCP, Azure) for large-scale data processing and model deployment.

Model Evaluation & Maintenance:

Continuously monitor model performance and retrain models as necessary.

Conduct regular performance testing and validation to ensure the accuracy and relevance of ML models.

Work on A/B testing to evaluate the impact of models in production environments.

Documentation & Reporting:

Document all development processes, model architectures, and data pipelines for future reference and reproducibility.

Prepare reports and presentations for stakeholders, summarizing findings and providing actionable insights.

Continuous Learning:

Stay updated on the latest advancements in machine learning, data analytics, and related technologies.

Experiment with new algorithms, tools, and technologies to improve model performance and the data pipeline.

Key Skills & Qualifications:

Technical Skills:

Programming Languages: Proficiency in Python (libraries such as NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, Keras, PyTorch).

Machine Learning Frameworks: Experience in implementing machine learning models using popular frameworks (e.g., TensorFlow, PyTorch, Scikit-learn, XGBoost).

Data Analytics Tools: Strong experience in data wrangling, exploratory data analysis, and visualization using tools like Pandas, Matplotlib, Seaborn, Plotly, and Tableau.

Statistical Analysis: Knowledge of statistical methods and algorithms (e.g., regression analysis, hypothesis testing, clustering).

Big Data Technologies: Familiarity with big data processing tools (e.g., Hadoop, Spark, Dask) is a plus.

Databases: Experience with SQL and NoSQL databases (e.g., MySQL, PostgreSQL, MongoDB) and proficiency in querying large datasets.

Cloud Computing: Experience with cloud platforms (e.g., AWS, Google Cloud, Microsoft Azure) for machine learning model deployment and data storage.

Machine Learning & AI:

Experience building, training, and evaluating machine learning models in areas such as classification, regression, clustering, natural language processing (NLP), or computer vision.

Strong understanding of model evaluation metrics (e.g., accuracy, precision, recall, F1 score, ROC-AUC).

Familiarity with deploying models into production environments and performing continuous monitoring.

Analytical Skills:

Strong problem-solving skills with the ability to translate complex business problems into technical solutions.

Ability to perform deep dives into data and generate insights that can impact business decisions.