Principal Data Scientist - IT

Looking for Female candidate

Main points: different techniques of ML, reasons behind techniques, who understands the logic/ maths behind the library/ function

Skills: python, deep learning

- 6 to 7 yrs core in data science

- B.tech/ B.E. / M.tech /Masters in Computer science - from good institutes

Don'ts: no finance domain

What we look for

Required

- Have analyzed, trained and deployed at least three data mining models in the past. If the candidate did not directly deploy their own models, they will have worked closely with others who have put their models into production. The models should have been validated as robust over at least an initial time period. Models that have only been used for a one-off don't count.

- 8+ years of industry work experience specifically developing data mining models which were deployed and used.

- Programming experience in Python is core using data mining related libraries like Scikit-Learn, NumPy, SciPy and Pandas. Past DS experience in R, SAS, Matlab or other systems is valid, but we need people comfortable in Python, and who can pass basic interview questions on Python.

Data mining algorithm experience in at least 3 algorithms within any of the bullets below:

- deep learning (TensorFlow/Keras, Convolutional Neural Nets (CNN) or related). Valid transferrable skills include PyTorch. Common architectures we use includes Transformers, LSTM, EfficientDet, YOLO, ResNet.

- prediction (statistical regression, neural nets, deep learning, decision trees, SVM, ensembles of models and so on),

- clustering (k-means, hierarchical, DBSCAN or other)

- Bayesian Networks

Preferred:

- Sets expectations, develops project plans and meets expectations.

- Experience adapting technical dialogue to the right level for the audience (i.e. executives) or specific jargon for a given vertical market and job function.

- Technical skills

- Commonly, candidates have a MS or Ph.D. in Computer Science, Math, Statistics or an engineering technical discipline. BS candidates with experience are considered.

- Data Science publications or patents. JCI is very supportive of filing patents and filed 550 patents in 2021.

- Have managed past models in production over their full life cycle until model replacement is needed. Have developed automated model refreshing on newer data. Have developed frameworks for model automation as a prototype for product.

- Training or experience in Deep Learning, such as TensorFlow (TF), Keras, PyTorch, ONNX, convolutional neural networks (CNN) or Long Short Term Memory (LSTM) neural network architectures. Our deployment systems support TensorFlow, so any PyTorch models will need to be converted with ONNX. If you don't have deep learning experience, we will train you on the job.

- Optimization of neural net inferencing to increase speed or reduce size, such as with TensorFlow, Intel's OpenVino, NVIDIA's TensorRT, TensorFlow Lite (for Android or ARM), TensorFlow Virtual Machine (TVM).

- Java for modelling or Android app development is used on the Android platform

- OpenCV or other image processing tools or libraries

- Cloud computing: Microsoft Azure, Amazon AWS or Google Cloud. Technologies like AutoML, Snowflake, HBase, Kubernetes

- Decision trees like XGBoost or Random Forests

- Complex Event Processing (CEP) for asynchronous streaming data.

- Time series algorithms from ARIMA to Digital Signal Processing (DSP).

- Bayesian Networks (BN), a.k.a. Bayesian Belief Networks (BBN) or Graphical Belief Networks (GBN), reasoning about causality.

- Vertical experience, which may involve industrial Internet of Things (IoT) applications:

- Smart buildings, Building Management Systems (BMS),

- Video Management Systems (VMS)

- HVAC systems and optimization, maintenance, optimizing energy consumption

- Physical security systems