Job Title:
Data Scientist
Location: Pakistan
Terms: Full-Time
Date of Posting: 01.05.2021
Valid Date: 30.05.2021
Data Scientist | Confiz Simplicant-May 2021
About Company:
Confiz Simplicant
We are committed to enabling people and verticals through high performance industry solutions and applications.
Throughout our history, we have expanded our offerings and capitalized on evolving technology trends to better serve our clients.
With our extensive team, massive technology portfolio, subject matter expertise in verticals, and delivery method, we work with clients to achieve the best fit between technology and their business.
About Role:
We are looking for an experienced Data Scientist. Apply only if you consider yourself to be the very best and are looking for challenges and rewards that no other company will provide.
Data Scientist | Confiz Simplicant-May 2021
Job Responsibilities:
We have an amazing team of 450+ individuals working on highly innovative enterprise projects & products. Our customer base includes fortune 5 retail and CPG companies, leading store chains, fast growth fintech and multiple Silicon Valley startups.
What makes Confiz stand out is our focus on processes and culture. Confiz is CMMi Level 3 and ISO 9001:2015 certified. We have a vibrant culture of learning via collaboration and making workplace fun.
People who work with us work with the cutting-edge technologies while contributing success to the company as well as to themselves. This is obviously above and beyond the usual stuff (excellent pay, medical benefits, frequent appraisals and bonuses).
Requirements :
- Very Good mathematical and statistical skills
- Proficiency in Python/R/MATLAB
- Able to do data wrangling and finding patterns in the data
- Can analyse large, complex datasets to extract insights
- Strong understanding of:
- Supervised and Unsupervised ML models.
- At least one of the three type of time series forecasting models:
- Classical ARIMA (auto regression and moving average) based forecasting models.
- Latest Neural Network (RNN like LSTM and other variations of RNN etc) based models.
- Hybrid models (lies between classical and NN based models)
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