Senior Machine Learning Engineer

• Design develop and deploy machine learning models and algorithms for time series data including forecasting anomaly detection and pattern recognition.
  
• Collaborate with crossfunctional teams including data engineers analysts and subject matter experts to understand business requirements and translate them into technical solutions.
  
• Perform exploratory data analysis feature engineering and data preprocessing tasks to prepare time series data for modeling
  
• Evaluate and select appropriate machine learning techniques such as ARIMA Prophet LSTM and other deep learning models for time series forecasting and analysis
  
• Optimize and tune machine learning models for accuracy performance and scalability
  
• Implement and maintain robust data pipelines for realtime and batch processing of time series data
  
• Conduct research and stay uptodate with the latest advancements in time series analysis and machine learning techniques
  
• Communicate complex technical concepts and findings to both technical and nontechnical stakeholders
  

Requirements

• Bachelors or Masters degree in Computer Science Statistics Mathematics or a related quantitative field
  
• Minimum of 5 years of experience in machine learning engineering with a strong focus on time series data analysis and forecasting
  
• Proficient in Python and relevant machine learning libraries such as TensorFlow PyTorch scikitlearn and pandas
  
• Solid understanding of statistical methods signal processing and time series analysis techniques
  
• Experience with big data technologies such as Apache Spark Hadoop or similar distributed computing frameworks
  
• Familiarity with cloud computing platforms (e.g. AWS GCP Azure) and containerization technologies (e.g. Docker Kubernetes)
  
• Strong problemsolving analytical and critical thinking skills
  
• Excellent communication and collaboration abilities
  
• Passion for staying uptodate with the latest advancements in machine learning and time series analysis
  

• Experience in specific industries or domains involving time series data (e.g. finance energy manufacturing IoT)
  
• Knowledge of deep learning techniques for time series forecasting such as LSTMs GRUs and attention mechanisms
  
• Experience with streaming data processing frameworks (e.g. Apache Kafka Apache Flink)
  
• Familiarity with machine learning model deployment and monitoring tools
  
• Publication record or contributions to opensource projects related to time series analysis or machine learning