Job Description
Join the fastest-growing field on the planet with a culture of performance, collaboration and opportunity. Develop technology that's going to change the lives of millions. Here, innovation is about bringing technology close to the end user - The farmers. There's no room for error. Join us and start doing your life's best work.
Roles and Responsibilities
- Work closely with leadership in devising strategy for pioneering and implementing advanced analytics methods to identify and solve business problems
- Deliver data science solutions leveraging latest machine learning techniques, including exploratory data analysis, feature engineering and selection, model selection, evaluation and cross validation, deployment and production at scale that drive value
- An avid technology enthusiast , Machine leaning methodology , Linear and hedonic regressions, random forest
- ML Model Must have Regression models, Random Forest, Boosting models . Good to have other advanced ML techniques knowledge SVM, Naive Bayes , KNN,
- Clustering techniques K means etc
- AI Deep Learning Must have some exposure to NLP, Text mining . Good to have Deep Learning Model knowledge e.g. ANN, CNN, LSTM
- Knowledge of SAS, R , python predictive analytics Data Modelling etc.
- Applying data mining techniques, doing statistical analysis, and building high quality prediction systems integrated with agriculture, ecommerce, banking, and insurance products.
- Selecting features, building, and optimizing classifiers using machine learning and deep learning techniques.
- Data mining using cutting edge technology stack.
- Enhancing data collection procedures to include information that is relevant for building analytical systems.
- Processing, cleansing, and verifying the integrity of data used for analysis.
- Doing ad-hoc analysis and presenting results in an intuitive manner.
- Creating automated anomaly detection systems and constant tracking of its performance.
- Liaison with Research and Innovation team, Agronomists and Crop Modellers.
Desired Candidate Profile
- Excellent understanding of machine learning techniques and algorithms, such as k-NN, Nave Bayes, SVM, Decision Forests, CNN, etc.
- Experience with data science toolkits, such as R, Python, Pandas, NumPy, Scikit-Learn, Keras, Tensor Flow. Hands-on experience in at least one of these is highly desirable.
- Experience with data visualization tools, such as D3.js, Matplotlib, Tableau
- Proficiency in using query languages such as SQL and SQLAlchemy ORM
- Experience with NoSQL databases, such as MongoDB, Cassandra
- Good knowledge in Python frameworks like Flask, Django, Scrapy, Beautiful Soup .
- Hands-on Experience in handling Big Data.
- Great communication skills