- DataScience: Data mining predictive modeling time series analysismachinelearning
- BigData: Big data methodologies data transformation/cleaning(structured &unstructured)
- Programming:Python (proficiencyessential)
- MachineLearning: Deep Learning techniques (LLMs a plus) classical machinelearning A/Btesting
- DataStorage & Processing: Spark Hive distributed data storagesystems (Hadoop BigQuery EMR) (relational databases a plus: OracleSAP DB2 Teradata MS SQL ServerMySQL)
- DataVisualization: Business intelligence and visualization tools(Tableau MicroStrategy Chartio Qlik) (geospatial data processing aplus)
- StrategicVisionary: Develop and lead the longterm data science roadmapfocusing on how data can optimize customer experiences and informproductenhancements.
- InsightsAlchemist: Drive indepth analysis of user behavior and the broaderecosystem identifying key levers to improve core metrics anddevelop predictivemodels.
- ModelMaestro: Shape and influence the development and implementation ofdata and machine learning models maximizing user experience anduncovering new avenues forinnovation.
- DataDrivenLeader: Translate complex data findings into clear actionablerecommendations for product and businessdecisions.
- ScalableSolutions: Design and implement productionready machine learningalgorithms for big dataenvironments.
- DiscoveryChampion: Spearhead exploratory data analysis projects that revealhidden patterns and unlock potential for growth andoptimization.
- DataDetective: Answer critical business questions by leveraging bigdata to analyze trends identify root causes and optimize productsandservices.
- MetricsMastermind: Define and track key performance indicators (KPIs) tomeasure projectsuccess.
- ExperimentationExpert: Design conduct and analyze A/B tests to validate hypothesesand informdecisionmaking.
- TechSavvyInnovator: Continuously explore and evaluate new data processingtechnologies staying at the forefront of industry bestpractices.
- LargeLanguage Champion: Build and deploy innovative applicationsleveraging cuttingedge large language models (LLMs). (Optional ifapplicable)
- 7years of experience in data science encompassing data miningpredictive modeling time series analysis machine learning big datamethodologies and data transformation/cleaning (structured andunstructured).
- Advanceddegree (PhD preferred) in a quantitative discipline (PhysicsStatistics Mathematics Engineering or ComputerScience).
- Deepexpertise in Deep Learning techniques (experience with LLMs asignificantplus).
- Provenproblemsolving and coding skills (Python proficiencyessential).
- Strongunderstanding of A/B testing classical machine learning and deeplearning.
- Solidfoundation in recommendation systems ranking and retrievalalgorithms.
- Indepthknowledge of Python SQL Spark andHive.
- Demonstratedexperience with distributed data storage systems like HadoopBigQuery EMR Hive etc. (Familiarity with relational databases likeOracle SAP DB2 Teradata MS SQL Server and MySQL is aplus).
- Experiencewith business intelligence and visualization tools (TableauMicroStrategy Chartio Qlik) is a plus. (Bonus points for geospatialdata processingskills)
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Senior Data Scientist - Bengaluru, India - Carnation Infotech Pvt Ltd
Description
Position Name:Senior Data ScientistExperience:7years
KeySkills to lookfor:
AbouttheRole:
Asa Senior Data Scientist youll play a pivotal role inrevolutionizing our customer acquisition and engagement strategies.Youll leverage your expertise in machine learning and big data tobuild robust models and lead exploratory analyses that unlock newgrowth opportunities. This leadership role demands excellentcommunication and collaboration skills as you partner with productmanagers and executives to translate data insights into actionablestrategies.
Responsibilities:
Qualifications:
Key Skills to look for: Data Science: Data mining, predictivemodeling, time series analysis, machine learning Big Data: Big datamethodologies, data transformation/cleaning (structured &unstructured) Programming: Python (proficiency essential) MachineLearning: Deep Learning techniques (LLMs a plus), classical machinelearning, A/B testing Data Storage & Processing: Spark, Hive,distributed data storage systems (Hadoop, BigQuery, EMR)(relational databases a plus: Oracle, SAP, DB2, Teradata, MS SQLServer, MySQL) Data Visualization: Business intelligence andvisualization tools (Tableau, MicroStrategy, Chartio, Qlik)(geospatial data processing a plus)