Data Science Leader
4 weeks ago
As a Data Science Leader, you will be responsible for leading the execution of new initiatives for Machine Learning using conversational NLP techniques and introducing ML-based learning for recommendation and coaching assistant in our products. You will also lead a team of ML engineers in evaluating ML technologies, building/training models, deployment of models, and monitoring in production environments.
Key Responsibilities include:
1. Lead and drive execution of new initiatives for Machine Learning using conversational NLP techniques and introduce ML-based learning for recommendation and coaching assistant in our products.
2. Lead team of ML engineers in evaluating ML technologies, building/training models, deployment of models, and monitoring in production environments.
3. Work with the engineering teams and ensure timely deliveries.
4. Be part of a global Engineering team supporting Fortune 1000 customers worldwide.
5. Ability to experiment and iterate rapidly and provide tangible improvements in the overall engagement.
We are looking for a driven team player, collaborator, and relationship builder whose infectious can-do attitude inspires others and encourages excellent performance in a fast-moving environment. Results orientated, motivated by success. Self-motivated: You can work with minimal supervision and be able to strategically prioritize multiple tasks proactively.
Requirements include:
1. Master's or bachelor's degree in computer science or a related study or equivalent experience.
2. 7+ years of hands-on experience architecting and designing highly scalable and resilient systems.
3. 5+ years of experience in designing & scaling applications based on Data Science, NLP, and conversational AI.
4. Understanding of ML algorithms - classical and deep learning and ML frameworks.
5. Good understanding of system availability, security, and performance management.
In particular, the candidate should have work experience with:
1. PyTorch and its ecosystem of libraries.
2. Word and document embeddings.
3. Transformers and Attention.
4. RNN, LSTM.
5. A background in BERT and its variants.
6. Transfer-learning practices.
7. NLP Libraries: NLTK, Genism, Spacy.
8. ML-pipelines – Apache Airflow/ Kubeflow/ RAY.
9. OpenAI libraries and Models - LLMs.
Besides this, the candidate must possess a mature understanding of, and hands-on experience in, the broad field of machine-learning and statistical methods. Must be conversant with:
1. scikit-learn.
2. Pandas.
3. Numpy.
4. Plotting using matplotlib, seaborn, etc.
5. HuggingFace.
Having experience in building recommendation systems with following algorithms will be considered as a plus:
1. Generative Models VAE & GAN.
2. Collaborative based filtering/ Context aware/Graph based recommender systems.
3. Factorization Machine.
4. Deep recommender.
Experience with any of the following is considered a plus:
1. Tensorflow.
2. Big-data and PySpark.
3. Data wrangling libraries: Beautiful Soup or Scrapy.
4. R libraries.
5. Taking models to production.
6. Cloud Technologies: GCP/AWS.
7. ML Graph Models GNN and NetworkX.
If you are a driven team player, collaborator, and relationship builder with a passion for Data Science, we encourage you to apply for this exciting opportunity.
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