Uber Hiring Decision Scientist Risk Job।Easy Apply

Job Title : Decision Scientist Risk

Salary : Rs 5.0 Lakhs to 15.0 Lakhs

Company : Uber

Location : Hyderabad, Telangana

Qualifications : Education in Engineering, Computer Science, Math, Economics, Statistics or equivalent experience

Experience : 4+ years of experience

ABOUT UBER

Uber Technologies, Inc., based in 2009, has revolutionized the transportation corporation globally. Headquartered in San Francisco, Uber operates a platform that connects riders with drivers via its cellular telephone app. Initially known for journey-hailing offerings, Uber has elevated its offerings to encompass food delivery (Uber Eats), freight transportation (Uber Freight), and electric powered bike and scooter leases (Jump). The employer’s commercial enterprise model focuses on providing convenient, reliable transportation options whilst leveraging technology to optimize operations and decorate man or woman experience.

Uber has faced regulatory demanding situations in various markets however keeps to innovate and enlarge its services worldwide. The business enterprise emphasizes safety and efficiency, incorporating capabilities such as GPS tracking, driving force history tests, and real-time trip monitoring. Uber’s impact extends past transportation, influencing city mobility tendencies and shaping the gig economy. With a dedication to sustainability and purchaser satisfaction, Uber remains a dominant player within the evolving panorama of mobility services.

Job Overview

A Decision Scientist Risk specializing in risk evaluation makes use of records-driven methodologies to assess and mitigate uncertainties in business selections. They appoint statistical modelling, possibility idea, and gadget studying strategies to quantify dangers and optimize techniques. Key responsibilities include developing predictive models, carrying out state of affairs analyses, and offering actionable insights to senior management. They collaborate throughout groups to decorate choice-making frameworks, making sure alignment with organizational desires and chance tolerance. Strong analytical skills, skill ability in programming languages (e.g., Python, R), and a strong expertise of economic markets or operational procedures are critical. Effective communique of complex findings to non-technical stakeholders is likewise crucial on this function.

Qualifications of Decision Scientist Risk:

1. Educational Background:
   Typically, a Master’s or Ph.D. In a quantitative subject which include Statistics, Mathematics, Economics, Operations Research, or Computer Science is preferred. A sturdy basis in chance concept, mathematical modelling, and statistics analysis is important.
   Courses or certifications in danger management, economic engineering, or associated fields may be beneficial.

Skills of Decision Scientist Risk:

2. Technical Skills:
   Statistical Modelling: Proficiency in superior statistical strategies which include regression analysis, time series analysis, Bayesian inference, and stochastic modelling.
   Machine Learning: Experience in applying ML algorithms for chance prediction and type, inclusive of ensemble techniques, neural networks, and decision trees.
   Programming Languages: Strong coding talents in languages like Python, R, or Julia for facts manipulation, statistical analysis, and version implementation.
   Data Management: Knowledge of SQL or other database querying languages for green records extraction and manipulation.
   Data Visualization: Ability to create clear and insightful visible representations of statistics using gear like maloti, plot, or Tableau.
3. Analytical Skills:
   Risk Assessment: Expertise in assessing and quantifying dangers thru probabilistic modelling, strain testing, scenario evaluation, and sensitivity evaluation.
   Optimization Techniques: Familiarity with optimization strategies such as linear programming, integer programming, and Monte Carlo simulations for decision aid.
   Critical Thinking: Ability to evaluate complex problems, discover key variables, and expand innovative solutions to mitigate dangers.
4. Domain Knowledge:
   Understanding of unique domains together with finance, insurance, supply chain management, or healthcare, depending at the enterprise recognition of the role.
   Knowledge of regulatory requirements and enterprise requirements associated with danger control and compliance.
5. Soft Skills:
   Communication: Effective communication abilities to translate complex technical findings into actionable insights for non-technical stakeholders.
   Collaboration: Ability to work across multidisciplinary groups which includes facts scientists, enterprise analysts, and senior control to integrate threat tests into strategic decision-making strategies.
   Problem-Solving: Strong trouble-solving abilities to address sudden demanding situations and adapt analytical procedures as wished.
   Project Management: Experience in dealing with initiatives related to hazard modelling, from records series and evaluation to presenting suggestions.
6. Tools and Platforms:
   Familiarity with statistical software program applications (e.g., SAS, SPSS, Stata) and libraries in Python (e.g., Numbly, pandas, scikit-analyze) or R (e.g., tidy verse, caret) for records evaluation and modelling.
   Knowledge of hazard management systems or gear used for financial risk evaluation (e.g., Bloomberg, Risk Metrics, MATLAB).
7. Ethical and Professional Standards:
   Understanding of moral concerns in information evaluation and chance control, including confidentiality, bias mitigation, and compliance with regulatory necessities (e.g., GDPR, HIPAA).
8. Continuous Learning:
   Willingness to live up to date with advancements in information technological know-how, danger evaluation methodologies, and enterprise tendencies thru self-study, meetings, or professional development guides.
9. Experience:
   Previous revel in in a quantitative analysis role, preferably in chance control, financial modelling, or related fields, imparting a sensible information of making use of analytical techniques to actual-global issues.

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