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Data Scientist in a Digital Ad Agency
Location : Worli, Mumbai
Experience : 5 - 8 years
Key Projects to Work on :
1. Digital Attribution (Marketing Effectiveness Projects)
2. Using vision apis for creative quality
3. Support on Automation
4. Support on building algorithms for new products
5. Dogfooding apis from new data sources
6. To lead the development of an Internal Product
Skills :
Technical Knowledge :
1. Python : Fluency in :
- Numpy, Scipy, Pandas packages
- API calls, throttling
- Basic web development (creating web services)
- Fetching and inserting from and to databases
- Understanding and manipulating existing ML libraries to suit specific use cases
2. Databases : Fluency in :
- SQL, MySQL, NoSQL/Mongo
- Creating and executing stored procedures
- Working with cloud database servers
- Creating data flows from raw database insertions to data manipulations
3. Statistics : Fluency in :
- Distribution types and their characteristics (Gaussian, poisson, T-distribution etc)
- Fitting data to these distributions
- Entire modelling cycle in R
- Plotting data to bring out insights and dashboard creation
- Feature engineering to explain business outcomes
- Deriving features that reduce model complexity and increase the quality of model outcomes
- Using the latest ML tools available and understanding the logic of the same so that they can be tactfully applied in products
4. Probability: Sound understanding of :
- Conditional Probability
- Bayes Rule
- Likelihood Independence
- Bayes Nets
- Markov Decision Processes
- Hidden Markov Models
5. Should have had experience in developing a product (coding and overall) of 2 years
Good to Have :
- Standard implementations of Machine Learning algorithms are widely available through libraries/packages/APIs (e.g. scikit-learn, Theano, Spark MLlib, H2O, TensorFlow etc.), but applying them effectively involves choosing a suitable model (decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models, etc.), a learning procedure to fit the data (linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods), as well as understanding how hyperparameters affect learning.
You also need to be aware of the relative advantages and disadvantages of different approaches, and the numerous gotchas that can trip you (bias and variance, overfitting and underfitting, missing data, data leakage, etc.)
Business Related :
1. Understanding of Digital Marketing
2. Slight knowledge of advertising
3. Understanding of how Facebook Marketing works.
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