Data Scientist

We are hiring for one of the MNC @ gurgaon, looking forward for a candidate who have good experience in NLP, Python, Machine learning concepts.

(a) Job Description NLP1. Roles and Responsibilities

The Data Scientist will be responsible for applying advanced data science algorithms for Natural Language Processing (NLP) and Understanding (NLU) to unstructured text data to create innovative business solutions.

She/he would apply supervised and unsupervised machine learning and deep learning algorithms to architect solutions for variety of enterprise data.

She/he would manage end-to-end data science project and lead a team of data scientist and big-data architects. Project management includes scoping project-objective in discussion with stakeholders, set goals, milestones, and timelines, solution architecture, and responsible for delivery. She/he would also be required to keep abreast with latest developments in the field, implement proof-of-concepts and prototypes to showcase new ideas.

Thinking strategically about uses of data for various business purposes, create innovative use-cases in discussion with various businesses.

Train/mentor junior data scientists, collaborate across projects, create IP for organization.

2. Required Skills

Expertise in information extraction (sentiment, intent, NER, relation, entity linking) from unstructured text data, logs etc. by applying NLP techniques.

Experienced in automatic summarization, topic segmentation and modeling using NLP algorithms. Automatic question-answering would be a plus.

Sequence/pattern mining from text, time series analysis using machine learning and deep learning algorithms.

Strong theoretical and practical knowledge of Machine Learning techniques like Nave Bayes, Random Forest, SVM, Gradient Boosting, Bagging, Feature engineering/selection, etc. Knowledge of Bayesian approach, Markov models, CRF is desirable.

Experienced in developing solutions using deep neural networks such as CNN/RNN. Experienced in NLP toolkits such as NLTK, Gensim, SpaCy, Stanford CoreNLP, Apache OpenNLP. Experience working with GPT API desirable.

Experienced in developing deep learning solution using Tensorflow/Keras APIs.

Strong background in algorithm development, creating data pipeline, and developing data visualization (dashboards).Experience working with databases (SQL/NoSQL/graph-based database).Strong problem solving and logical skills. Ability to think creatively, lead a team of data scientists and big data architect, and work as an individual contributor

(b) Job Description (Time Series) Roles and Responsibilities

- Data Scientist will be responsible for development and delivery of end-to-end data science solutions for time-series analysis approaches with Machine Learning and Deep Learning/AI algorithms.

- She/he would proactively interact with stakeholders and customers to understand business requirement, architect solutions to create high-quality high-impact data science solutions. Apart from hands-on work as an individual contributor (IC), she/he would also be responsible for overall project management including setting goals, milestones, timelines, and delivery.

- She/he would also be required to keep abreast with latest developments in the field, implement proof-of-concept and prototypes to showcase new ideas.

- Thinking strategically about use of data for various business purposes, create innovative use-cases in discussion with various business leaders.

- Train and mentor junior data scientists, collaborate across projects, create IP for organization.

Skills(a) Essential skills (must)

- Experience and expertise in time-series analysis of sensor data (telematics/vehicle data, machine data, utility data etc.) that includes generating real-time statistics, pattern mining, anomaly detection, forecasting etc.

- Experience in building production-level models with time-series data using Machine Learning (ML) or Deep Learning (DL) algorithms or probabilistic methods (HMM).

- Hands-on experience on using various algorithms for supervised and unsupervised machine learning, good understanding of the mathematical framework of the algorithms, statistics and probability.

- Experience in handling high-frequency large volume data on cloud, developing and deploying ML/DL solutions on cloud.

- Technologies: Proficiency in Python/R and Spark. Hands-on experience in working with different APIs such as TensorFlow/Keras/Torch for DL, use of SparkML, Scikit, Numpy, Scipy, pandas for ML. Hands-on experience with AWS / GCP/ Azure / IBM.

Neha Chatwal