{"id":3281795,"url":"https://alion.io/job/idfc-first-bank-senior-data-scientist-4","title":"Senior Data Scientist","company":{"id":720227,"name":"IDFC FIRST Bank","domain":"idfcfirst.bank.in","url":"https://alion.io/company/idfcfirst","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India","Mumbai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":21000,"max_usd":46000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":75},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly 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activities such as data cleaning, merging enrichment, etc.\nPerform feature engineering by extracting meaningful features from measured and/or derived data.\nPerform exploratory and targeted data analyses to get key insights.\nBuild Stochastic and Machine-learning algorithms that potentially address business problems.\nLead and implemented Machine Learning projects from initiation through completion with a particular focus on automated deployment and ensuring optimized performance.\nMaintain and optimize the machine learning/deep learning models developed by the Data Scientist and ensure seamless deployment in different environments (Dev/QA/Prod) while enabling model tracking, model experimentation, and model automation.\nCollaborate with the data engineers and data scientists on model development to containerize and build out the deployment pipelines for new models.\nCollaborate on MLOPS life cycle and experience with MLOPS workflows and traceability and versioning of datasets.\nEnsure traceability and versioning of datasets, models, and evaluation pipelines.\nDesign, prototype, build, and maintain APIs for consumption of machine learning models at scale.\nFacilitate the development and deployment of POC machine-learning systems.\nUsing standard methodologies framework to ensure data quality and reconciliation checks are in place and are transparent to everyone.\n\nRequirements: \nStrong understanding of advanced data mining techniques, curating, processing, and transforming data to produce sound datasets.\nStrong understanding of the Machine Learning lifecycle - feature engineering, training, validation, scaling, deployment, scoring, monitoring, and feedback loop.\nExperience in analyzing complex problems and translating them into an analytical approach.\nExperience in Supervised and Unsupervised Machine Learning including Classification, Forecasting, Anomaly Detection, Pattern Detection, and Text Mining, using a variety of techniques such as Decision trees, Time Series Analysis, Bagging and Boosting algorithms, Neural Networks, and Deep Learning.\nExperience with analytical programming languages, tools, and libraries (Python ecosystem preferred, but R will be considered).\nHas used frameworks/libraries such as Scikit-learn, PyTorch, Keras, and NLTK.\nDeep understanding of data structures and algorithms.\nExcellent programming skills in Python and PySpark.\nHands-on experience using Spark (in Python) - to develop high-volume ETL pipelines.\nHave implemented machine learning projects from initiation through completion with a particular focus on automated deployment and ensuring optimized performance.\nHave maintained and optimized the machine learning/deep learning models developed by the Data Scientist and ensured seamless deployment in different environments while enabling model tracking, model experimentation, and model automation.\nExperience in SQL and relational databases, Big Data technologies e. g., Spark/Hadoop and Cloud technologies.\nHands-on experience with workflow management tools such as Airflow.\nA strong understanding of RESTful APIs and/or data streaming is a big plus.\nRequired experience in modern version control (GitHub, Bitbucket).\nHands-on experience with containerization (Docker, Kubernetes, etc. ).\nAt least 5+ years of professional experience as a data scientist or ML Engineer.\n\nEducational Qualifications: \nAdvanced degree in an analytical field (e. g., Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, Data Analysis, Operations Research, Artificial Intelligence, or Applied Math).\nSkills\nML, MLOps, deployment, docker, kubernetes, machine learning, mlops, pyspark, python, 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