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DA DS Trainer JD:
Key Responsibilities:
Develop and deliver comprehensive training programs on data science topics, including machine learning, artificial intelligence, big data, data mining, and predictive analytics.
Create engaging and interactive training materials, including presentations, hands-on exercises, case studies, and assessments.
Conduct classroom, online, and one-on-one training sessions for students at different skill levels.
Evaluate students' performance and progress, providing feedback and support to help them improve.
Stay up-to-date with the latest trends and best practices in data science and incorporate them into training programs.
Collaborate with other trainers and industry experts to continuously improve training content and methodologies.
Assist in the development of training schedules and manage training logistics.
Provide mentorship and career guidance to students aspiring to build a career in data science.
Qualifications:
Bachelor’s degree in Data Science, Computer Science, Statistics, or a related field; a Master’s degree is a plus.
Proven experience as a Data Science Trainer or a similar role.
In-depth knowledge of data science concepts and hands-on experience with tools like Python, R, TensorFlow, and Hadoop.
Excellent presentation and communication skills.
Strong organizational and time-management abilities.
Ability to simplify complex concepts and deliver them in an understandable manner.
Certification in Data Science (e.g., Microsoft Certified: Azure Data Scientist Associate) is preferred.
Preferred Skills:
Experience with LMS (Learning Management Systems) and e-learning platforms.
Strong analytical and problem-solving skills.
Ability to adapt training methods to different learning styles.
Passion for continuous learning and professional development.
Benefits:
Competitive salary and performance-based incentives.
Professional development opportunities.
Flexible working hours.
Collaborative and innovative work environment.
Job Description:
1.Be a hands on problem solver with consultative approach, who can apply Machine Learning & Deep Learning algorithms to solve business challenges
a. Use the knowledge of wide variety of AI/ML techniques and algorithms to find what combinations of these techniques can best solve the problem
b. Improve Model accuracy to deliver greater business impact
c.Estimate business impact due to deployment of model
2.Work with the domain/customer teams to understand business context , data dictionaries and apply relevant Deep Learning solution for the given business challenge
3.Working with tools and scripts for sufficiently pre-processing the data & feature engineering for model development – Python / R / SQL / Cloud data pipelines
4.Design , develop & deploy Deep learning models using Tensorflow / Pytorch
5.Experience in using Deep learning models with text, speech, image and video data
a.Design & Develop NLP models for Text Classification, Custom Entity Recognition, Relationship extraction, Text Summarization, Topic Modeling, Reasoning over Knowledge Graphs, Semantic Search using NLP tools like Spacy and opensource Tensorflow, Pytorch, etc
b.Design and develop Image recognition & video analysis models using Deep learning algorithms and open source tools like OpenCV
c.Knowledge of State of the art Deep learning algorithms
6.Optimize and tune Deep Learnings model for best possible accuracy
7.Use visualization tools/modules to be able to explore and analyze outcomes & for Model validation eg: using Power BI / Tableau
8.Work with application teams, in deploying models on cloud as a service or on-prem
a.Deployment of models in Test / Control framework for tracking
b.Build CI/CD pipelines for ML model deployment
9.Integrating AI&ML models with other applications using REST APIs and other connector technologies
10.Constantly upskill and update with the latest techniques and best practices. Write white papers and create demonstrable assets to summarize the AIML work and its impact.
· Technology/Subject Matter Expertise
- Sufficient expertise in machine learning, mathematical and statistical sciences
- Use of versioning & Collaborative tools like Git / Github
- Good understanding of landscape of AI solutions – cloud, GPU based compute, data security and privacy, API gateways, microservices based architecture, big data ingestion, storage and processing, CUDA Programming
- Develop prototype level ideas into a solution that can scale to industrial grade strength
- Ability to quantify & estimate the impact of ML models.
· Softskills Profile
- Curiosity to think in fresh and unique ways with the intent of breaking new ground.
- Must have the ability to share, explain and “sell” their thoughts, processes, ideas and opinions, even outside their own span of control
- Ability to think ahead, and anticipate the needs for solving the problem will be important
· Ability to communicate key messages effectively, and articulate strong opinions in large forums
· Desirable Experience:
- Keen contributor to open source communities, and communities like Kaggle
- Ability to process Huge amount of Data using Pyspark/Hadoop
- Development & Application of Reinforcement Learning
- Knowledge of Optimization/Genetic Algorithms
- Operationalizing Deep learning model for a customer and understanding nuances of scaling such models in real scenarios
- Optimize and tune deep learning model for best possible accuracy
- Understanding of stream data processing, RPA, edge computing, AR/VR etc
- Appreciation of digital ethics, data privacy will be important
- Experience of working with AI & Cognitive services platforms like Azure ML, IBM Watson, AWS Sagemaker, Google Cloud will all be a big plus
- Experience in platforms like Data robot, Cognitive scale, H2O.AI etc will all be a big plus