Jinesh Maloo

• Committed ECU Validation & Verification Engineer with a total of 8 years of experience, including 4 years dedicated to crafting and implementing validation master plans. Experienced in drafting and endorsing validation protocols (CAN ,CANFD , Ethernet ,DOIP) , as well as collaborating with team members to ensure successful outcomes.
• Skilled in utilizing diagnostic tools and software tools like CANoe to pinpoint issues precisely. Proficient in delivering top-notch customer service and conveying technical information effective.
• Stellar problem-solving, decision-making and communication skills. Proven skills in completing Regression Testing , SW performance testing , Integration Testing. Background writing and implementing test cases, developing testing plans and identifying use cases based on customer requirements.
• Organized and dependable candidate successful at managing multiple priorities with a positive attitude. Willingness to take on added responsibilities to meet team goals.

Data Analyst Nanodegree Udacity

Project-1 ( During Onsite Term from Jan 2020 - current )

Validation & verification of Gateway ECU

Client: NISSAN Motors and Renault

Description:

• To validate all the functionalities as per GW Requirements defined by Nissan for upcoming new Secure Gateway ECU on Test bench using Vector Hardware & SW devices

Responsibilities:

• Requirement gathering & analysis , Test plan creation,Test execution,issue reporting ,Test report documentation
• Creation of canoe configuration & Performed Canoe simulation for functional testing and validation.
• Diagnostics service test for GW by diagnostic tools like DDT
• Created automation tool to analyze .asc files saving 90% of time
• As an OEM interface reporting SW quality issues for vehicle milestone events & handling of Milestone Quality Judgment meetings

SW & HW Tools:

• Canoe
• Caple scripting
• DDT
• vTest Studio
• VN -5650 ,1600 , 8912A
• VT System

Projects During Offshore term

Project 2:

Early Detection of Large scale Failures

Client: NISSAN Japan

Description:

• Using Diagnostic Data Collected from Market By Automotive OEM Builded an Early failure detection algorithm using Statistical methods to Identify DTC with abnormal increase in occurrence rate and Indicate alert to respective ECU design team regarding High occurrence DTC
• Also Made an Algorithm using R script to Link Warranty Data with Diagnostic Data to perform Machine Learning task like Association Analysis to identify relationship between high occurrence DTC and failed parts in Vehicles and Provide alert for associated failed parts to ECU design team for further analysis

Responsibilities:

Working in development, and enhancement ofAlgorithm as per needs

Tools: R language, SQL, Tableau, Microsoft excel

Project 3:

Project:

MFET Predictive Maintenance

Client: Mitsubishi-motors Japan

Description:

In This Use case of MFET predictive maintenance methodology we carried out task using machine learning algorithm and BI Tools following questions asked by customer1) What failure could occur within next 24 hours for my vehicle?2) Vehicle Usage Dash Board Using Tableau 3) Driver Behavior Dash Boards for Fleet Owners

Responsibilities:

Processing of MN Data, Building Data Aggregation and FeatureEngineering Mechanisms and Building ML Model on ProcessedData,Visualization Creation

Project 3:

Diagnostic Data Analysis

Client: Jaguar Land Rover (JLR) –UK

Description:

In this Project Customer came with Just there Diagnostic Data basically they want to do analysis on their Diagnostic Data to come up with insights which will be helpful to them So we performed three task1) Preliminary Analysis Objective : Identify data set for analysis 2) Exploratory Data Analysis – Objective: Understand behavior of variables associated with DTCs3) Machine Learning – Objective: Identify patterns that aid causal analysis for Particular DTCs

Responsibilities:

To Perform Processing of Data, Preliminary and Exploratory analysis and Use Machine learning Techniques to Find Insights from Data

Tools: Tableau ,R language, Python, Microsoft excel.