Powering the Next Era of EV Charging
Driving innovation and seamless connections across North America.
Powering the Next Era of EV Charging
Driving innovation and seamless connections across North America.
RébeccaChargeHub aggregates data from different sources to display EV drivers where to locate public charging stations. To display if a charging station is available or not; ensure that the charging locations have the right names, the right descriptions, the correct addresses; if other EV drivers left comments and so on. So my job is to make sure that we have as much quality information as we can get.
The fact that we aggregate data from many several sources adds complexity to how we display the data. For example, each of the charging networks displayed on the ChargeHub interactive map—such as Circuit électrique, FLO and many more—each have a specific way of recording their information into data models that are then provided to us. Our role is to read this data and interpret it into information that can be understood by the EV community who use the ChargeHub map.
So, my goal is to make sure that the data processes that we have worked well and are consistent when we need to unite data from different sources. There are many data structures that could do the same job, but there are some that are more efficient than others—that are more consistent than others.
That's what I look for. The most robust solution.
WeiOn a typical day, I analyse data. I provide analytics and data models that can be used for better decision-making when it comes to deploying the public charging infrastructure. The players involved include governments, cities, charging networks, utilities and more. With analytics, we can better understand when and where to add new charging stations.
For example, I look at the information we have in our database on charging session usage rates. Among the Level 2 and Level 3 (DCFC) charging stations displayed on our interactive map, some charging sessions are recorded for certain charging stations operated by partner charging networks.
These contain a lot of information that reveals charging behaviours that help to better understand peak demand times, or the busiest time of the year for a specific charging location and so on.
These analyses are important for several reasons. Have you ever waited in line to plug your EV into a fast-charging station on the highway? This is an example of a scenario we aim to eliminate with models and analytics that will signal the right times to add new charging stations. These analyses are also important for several other aspects. Notably, the increase in EVs on the road goes hand in hand with an increase in electricity demand. Some players in the EV ecosystem are preparing with data analytics to meet this new demand.
FannyEvery day, ChargeHub receives data from several sources that notify us to either: add new public charging stations; modify current charging locations; remove existing charging stations from the map and more. I’m the first one to validate this information.
For example, charging networks such as ChargeLab, eCharge, SemaConnect and so on, can provide us with data that indicates the name of a new charging location; a change in the charging costs at an existing public charging station; or modifications to the description of a charging station.
Now while the data science team at ChargeHub tries to make sure that the quality of the data is as good as possible, there will always be gaps. That's why we try to engage EV drivers as much as possible; so that they continue to submit feedback and contributions on the charging stations displayed on our interactive map.
In fact, I’m the one who processes the contributions submitted by the EV community. For example, EV drivers will sometimes edit the name of a charging location or change the address of an existing charging station. Other times, an EV driver might modify the description of an existing charging station, add new details to a newly installed charging station or give directions to the charging station.
I check everything: so thank you to all the EV drivers who contribute information to our interactive map. We really appreciate it!
RébeccaChargeHub aggregates data from different sources to display EV drivers where to locate public charging stations. To display if a charging station is available or not; ensure that the charging locations have the right names, the right descriptions, the correct addresses; if other EV drivers left comments and so on. So my job is to make sure that we have as much quality information as we can get.
The fact that we aggregate data from many several sources adds complexity to how we display the data. For example, each of the charging networks displayed on the ChargeHub interactive map—such as Circuit électrique, FLO and many more—each have a specific way of recording their information into data models that are then provided to us. Our role is to read this data and interpret it into information that can be understood by the EV community who use the ChargeHub map.
So, my goal is to make sure that the data processes that we have worked well and are consistent when we need to unite data from different sources. There are many data structures that could do the same job, but there are some that are more efficient than others—that are more consistent than others.
That's what I look for. The most robust solution.
WeiOn a typical day, I analyse data. I provide analytics and data models that can be used for better decision-making when it comes to deploying the public charging infrastructure. The players involved include governments, cities, charging networks, utilities and more. With analytics, we can better understand when and where to add new charging stations.
For example, I look at the information we have in our database on charging session usage rates. Among the Level 2 and Level 3 (DCFC) charging stations displayed on our interactive map, some charging sessions are recorded for certain charging stations operated by partner charging networks.
These contain a lot of information that reveals charging behaviours that help to better understand peak demand times, or the busiest time of the year for a specific charging location and so on.
These analyses are important for several reasons. Have you ever waited in line to plug your EV into a fast-charging station on the highway? This is an example of a scenario we aim to eliminate with models and analytics that will signal the right times to add new charging stations. These analyses are also important for several other aspects. Notably, the increase in EVs on the road goes hand in hand with an increase in electricity demand. Some players in the EV ecosystem are preparing with data analytics to meet this new demand.
FannyEvery day, ChargeHub receives data from several sources that notify us to either: add new public charging stations; modify current charging locations; remove existing charging stations from the map and more. I’m the first one to validate this information.
For example, charging networks such as ChargeLab, eCharge, SemaConnect and so on, can provide us with data that indicates the name of a new charging location; a change in the charging costs at an existing public charging station; or modifications to the description of a charging station.
Now while the data science team at ChargeHub tries to make sure that the quality of the data is as good as possible, there will always be gaps. That's why we try to engage EV drivers as much as possible; so that they continue to submit feedback and contributions on the charging stations displayed on our interactive map.
In fact, I’m the one who processes the contributions submitted by the EV community. For example, EV drivers will sometimes edit the name of a charging location or change the address of an existing charging station. Other times, an EV driver might modify the description of an existing charging station, add new details to a newly installed charging station or give directions to the charging station.
I check everything: so thank you to all the EV drivers who contribute information to our interactive map. We really appreciate it!
RébeccaChargeHub aggregates data from different sources to display EV drivers where to locate public charging stations. To display if a charging station is available or not; ensure that the charging locations have the right names, the right descriptions, the correct addresses; if other EV drivers left comments and so on. So my job is to make sure that we have as much quality information as we can get.
The fact that we aggregate data from many several sources adds complexity to how we display the data. For example, each of the charging networks displayed on the ChargeHub interactive map—such as Circuit électrique, FLO and many more—each have a specific way of recording their information into data models that are then provided to us. Our role is to read this data and interpret it into information that can be understood by the EV community who use the ChargeHub map.
So, my goal is to make sure that the data processes that we have worked well and are consistent when we need to unite data from different sources. There are many data structures that could do the same job, but there are some that are more efficient than others—that are more consistent than others.
That's what I look for. The most robust solution.
RébeccaI've always had a passion for mathematics and everything that’s related to deep thinking and problem-solving. So, once I finished my bachelor's degree in mathematics, I specialized in statistics. In statistics, you learn general techniques that can be applied to an incredibly varied set of fields. That's what I like; and I really like the logic of the structure.
What's interesting is asking the right questions. It's really about answering the right question and making sure that the person who wants to do their statistical analysis can pinpoint their problem and will understand what statistics can do for them and what can't.
WeiFrom a young age, I loved mathematics and science. I majored in mathematics and minored in economics in the undergraduate stage and graduated with double bachelor's degrees. In addition, I am granted a master's degree in mathematics and then acquired a second master's degree in computer science with a specialization in artificial intelligence.
Studying is interesting and makes me happy; you can get a lot of ideas from it. It's interesting because if we don't use any models or mathematics, we just look at the data by eyes. It's boring and superficial. We can't get actionable insights this way. But if we apply an algorithm or a model, we can get very interesting and informative results. When we look at the result, we may even need to think deeper to understand why this is the result. There's a lot of logic behind it, which always piqued my curiosity.
FannyEver since I was a kid, I have loved data and have always loved solving math problems. It's like doing a puzzle. Once I solve it, once I finish it, I feel a sense of accomplishment. I took an online data analysis course and learned the data analysis software called SAS on my own—in parallel to the education I got from Concordia University. When I noticed this job opening at ChargeHub, I was excited by the thought of working in the electric vehicle industry. It’s a fascinating field!
In China, where my family lives, everyone is talking about EVs. The last time I was there, I noticed an increase in the number of electric cars on the road right now. Even my sister is planning on buying an electric car.
So I applied, got the offer and that's how I started!
FannyI love to garden. I live in Beaconsfield and since last year I have planted a lot of vegetables in my garden. In the summer, I don't buy any vegetables, my garden provides me with everything I need to cook!
RébeccaFor now, I would tell you that one of my greatest joys and hobbies is taking care of my children every day. Seeing them develop individually and together as siblings.
They are very small, so every day they have wonder in their eyes. It’s amazing!
For these three women, who as children always had an enthusiasm for mathematics, the world of data science pairs passion and curiosity. The ChargeHub Data Science team remains as ambitious as ever to make your charging experience simpler and more enjoyable, by accessing the best quality information possible. As always, we encourage you to submit contributions to the ChargeHub app. They, in turn, will serve to guide our Data Scientists in providing the best information!
WeiMuch of my life has been driven by my choice to work in math or science. After so many years, I realize that the fact that I didn't choose art or music doesn't mean I don't like the arts. I am curious about the arts as well. So, I do plenty of art activities and visit museums in my spare time. My favourite is the famous Montreal Museum of Fine Arts!
FannyI love to garden. I live in Beaconsfield and since last year I have planted a lot of vegetables in my garden. In the summer, I don't buy any vegetables, my garden provides me with everything I need to cook!
RébeccaFor now, I would tell you that one of my greatest joys and hobbies is taking care of my children every day. Seeing them develop individually and together as siblings.
They are very small, so every day they have wonder in their eyes. It’s amazing!
For these three women, who as children always had an enthusiasm for mathematics, the world of data science pairs passion and curiosity. The ChargeHub Data Science team remains as ambitious as ever to make your charging experience simpler and more enjoyable, by accessing the best quality information possible. As always, we encourage you to submit contributions to the ChargeHub app. They, in turn, will serve to guide our Data Scientists in providing the best information!
WeiMuch of my life has been driven by my choice to work in math or science. After so many years, I realize that the fact that I didn't choose art or music doesn't mean I don't like the arts. I am curious about the arts as well. So, I do plenty of art activities and visit museums in my spare time. My favourite is the famous Montreal Museum of Fine Arts!
FannyI love to garden. I live in Beaconsfield and since last year I have planted a lot of vegetables in my garden. In the summer, I don't buy any vegetables, my garden provides me with everything I need to cook!
For these three women, who as children always had an enthusiasm for mathematics, the world of data science pairs passion and curiosity. The ChargeHub Data Science team remains as ambitious as ever to make your charging experience simpler and more enjoyable, by accessing the best quality information possible. As always, we encourage you to submit contributions to the ChargeHub app. They, in turn, will serve to guide our Data Scientists in providing the best information!
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