Fresher - Data Science Engineer - Busigence Technologies

This is a remote position.

Please go through the entire job post thoroughly before pressing Apply. Post pressing Apply, you shall reach the assessment page that must be attempted.

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Busigence is a Decision Intelligence Company. We create decision intelligence products for real people by combining data, technology, business, and behaviour enabling strengthened decisions.

Team: Sciences

Location: Remote

Relevant Exp: 0-2 Years

Background: Been there-Done that

Compensation: Above industry standards

Requirements

Remote position (work-from-anywhere)

Immediate joiners must apply

Data Science Experienced - course/competitions//internships/job (<2 years)

Competitive compensation

1. Code in Python3 - Numpy?

2.Code in Python3 - Pandas?

3.Code in Python3 - Scikit-learn?

4.Implemented full-cycle data science on real-world problem (not just academic or kaggle projects)?

5.Implemented SQL queries

6.Developed algorithms in Python3

7.Confidence to learn PySpark3 within a month? https://spark.apache.org/docs/latest/api/python/getting_started/index.html (we shall guide but won't spoon-feed)

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We are offering one of the most challenging & exciting work on Applied ML. You shall be working on sophisticated platforms, products and applications

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We are looking for developer with real passion for data science, machine learning and automation. This is a specialist and individual contributor role. Product development experience preferably at a startup or a lean team is desired

ROLE

Mandatory

1. Building applications for data preparation that includes impurities removal, anomaly detection, identifying inconsistencies and tranformations

2. Building applications for data exploration that includes missing value imputation, outlier analysis, class imbalance, correlation, and visualization

3. Building applications for feature engineering that includes feature generation, feature transformation, feature selection

4. Building applications for machine learning modeling that includes models development, hyperparameter optimization, model selection, training, validation and prediction

5. Building applications for machine learning automation that includes automating components included in each of the applications and automating integrated data science pipeline

6. Building sophisticated deterministic, stochastic and neural network models from scratch, in