Machine Learning Engineer-Predictive Analytics

3 weeks ago

Chennai, India OptiSol Business Solutions Full time

Experience: 2 to 5 years

Salary Range: 6 to 12 LPA


About OptiSol:

OptiSol is a full services digital solutions partner for Start-ups and Enterprises worldwide. We work on cutting edge digital solutions – AI/ML, Modern Web and Mobile, Enterprise Cloud and Microservices. We are 100% Agile embracing modern application development strategies.

People and Culture are our greatest differentiators. We are growing stronger with 300+ team focused on new age software development for the digital world. We invite people with Growth Mindset to join us in our endeavour to create a better world through cutting edge digital solutions for our customers.


Position Summary:

We are seeking a highly skilled and motivated Machine Learning Engineer with a specialization in Machine Learning and Time Series Analytics to join our team. In this role, you will be responsible for developing and implementing machine learning models and algorithms to analyze and predict time-dependent data patterns. You will collaborate with cross-functional teams to understand business requirements, design scalable solutions, and deploy models into production environments. The ideal candidate should have a strong background in machine learning, statistical analysis, and programming, with a focus on time series data.

OptiSol DataLabs, the AI/ML team here, builds cutting edge Date Analytics, Machine Learning and Deep Learning solutions for our worldwide customers.

We are a tribe of happy people excited about problem-solving. Our team is geographically distributed, we speak many languages and we come from a variety of culture backgrounds. We are rapidly expanding, and we are looking for passionately curious people obsessed with customer happiness.


Skills Required:

  • In depth knowledge of Python and its data analysis and machine learning packages like Scikit Learn, statmodels, TensorFlow, Keras, PyTorch, etc.
  • Strong understanding of machine learning techniques and algorithms, including but not limited to:
  1. Time series forecasting algorithms such as ARIMA, SARIMA, Exponential Smoothing methods (Holt-Winters), Prophet, LSTM (Long Short-Term Memory), and GRU (Gated Recurrent Unit).
  2. Anomaly detection algorithms such as Isolation Forest, One-Class SVM (Support Vector Machine), Local Outlier Factor (LOF), and Autoencoder-based methods.
  3. Clustering algorithms like K-means clustering, DBSCAN, and hierarchical clustering for pattern recognition in time series data.
  4. Profound knowledge of statistical methods and tools for data analysis, hypothesis testing, and model evaluation, including probability distributions, hypothesis testing, confidence intervals, and A/B testing.
  5. Familiarity with time series data preprocessing techniques such as differencing, smoothing, seasonality decomposition, and feature engineering to extract meaningful patterns and features.
  6. Experience with deep learning algorithms like RNN, LSTM, etc. especially in sequence prediction and time series analysis.
  7. Strong mathematical foundations including linear algebra, calculus and probability theory.
  • Contribute to data strategy for our clients through research into best practices or prior experience.
  • Prior exposure to cloud platforms (preferably AWS) to deploy data pipelines and to use tools/services like AWS SageMaker, Kinesis, MLFlow etc.,
  • Good familiarity with building RESTful APIs using frameworks like FastAPI/Flask/Django.
  • Proficient understanding of code versioning tools, such as Git/Bitbucket.
  • Experience with SQL/NoSQL databases are a plus.
  • Aptitude and Motivation is learning new tech stack and platforms.

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