Job responsibilities
- Develop and implement predictive models and machine learning algorithms to solve business problems.
- Validate and refine models to ensure accuracy and reliability.
- Clean, preprocess, and organize data for analysis.
- Develop and maintain data pipelines to ensure efficient data flow and accessibility
- Create clear and compelling data visualizations to communicate findings.
- Develop and present reports and dashboards to stakeholders.
- Work closely with cross-functional teams, including product managers, engineers, and business analysts.
- Communicate complex technical concepts to non-technical stakeholders.
- Stay updated with the latest advancements in data science and machine learning.
- Continuously improve processes, tools, and methodologies to enhance data science capabilities.
- Analyze large and complex data sets to identify patterns, trends, and insights.
- Interpret data to provide actionable recommendations to stakeholders
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 3+ years applied experience
- Regression and Classification models, Ensemble Methods, model evaluations
- Fully connected and Sequence Deep Learning model,Tensorflow/Pytorch
- NLTK, Spacy, Huggingface transformer.NLP classification, sentiment analysis, Named Entity Recognition, Autoencoder.Working experience in Model Monitoring, Explainable AI (XAI) and MLOps Lifecycle
- Data drift, concept drift.Explainability, Causal Analysis, Model fairness.Model deployment, model and data version. Excellent problem-solving abilities.
- Proven experience in building and deploying machine learning models.
- Proficiency in programming languages such as Python
- Experience with machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch)
- Strong skills in SQL and experience with databases.Experience with data visualization tools (e.g., Tableau, Power BI, matplotlib)
- Strong statistical and mathematical skills.Ability to analyze and interpret complex data sets.
- Experience in Anomaly Detection, NLP.Anomaly Detection techniques using ML and DL
Preferred Qualifications:
- Experience with big data technologies (e.g., Hadoop, Spark).
- Knowledge of cloud platforms (e.g., AWS, Azure, Google Cloud).
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field. A Ph.D. is a plus.
- Good to have: experience in LLM
- LLM Architecture, fine tune models, LLM evaluation
- RAG, Vector DB, Deploying LLM