'Be willing to be wrong' Caitlin Smallwood, VP Netflix

"Be willing to take a personal risk; be willing to be wrong".

Caitlin Smallwood says women must be willing to risk embarrassment to get ahead in a career; even if you sometimes get it wrong… speaking up builds confidence.
Caitlin is VP of Science & Algorithms at Netflix which has about 69 million customers. She spoke to TC about protecting data privacy, and how her love for math makes her ‘a pattern junkie’.

What is your role at Netflix?

I lead a group that’s a data science group. It's called Science & Algorithms at Netflix. We do everything ranging from algorithm research and development to predictive models that are leveraged internally for decision making, and all the experimentation … AB testing kind of methodologies, etc.

How does your work impact the Netflix consumer?

Ah well! Hopefully it means that the product is getting better and better for you and that the recommendations that you are seeing on the service are titles that you would enjoy watching, and hopefully, that’s getting better over time such that if you were a member of Netflix a year from now, your recommendations are even more perfect for you and letting you find great stuff. It’s helping us get content into the service that you will really enjoy. It’s helping to inform our content plan around what types of content do you want to satisfy everyone’s tastes.

What are the challenges facing women entering your field? Are there fewer women in data science?

I think it’s about the same for data science as in any deeply technical field where we face the kind of challenges you’ll see differentially during the course of people’s career evolution. If you look at people who enroll in programs I think you’ll find that it's almost 50 50 in those early stages and then as people progress through their careers, women do drop off at each sort of level.  So that’s the area for all tech fields - it’s really important to try to make the career more accessible over time.
Part of it is the natural thing of how people want to handle the phase of their lives when they’re having kids – that’s a challenge, right? And a lot of that (as many people have written about) has to do with society shifting so that more men take on more responsibility during that time. It’s certainly also about other things – like companies and what they can do to make sure the environment is flexible  a practical environment for women to be in their careers, during those times.

How would you describe the transitions in your own professional development in choosing to pursue your career?

I think that for me, personally, there was a phase of reaching a really different level of confidence that was really important. The ability to take personal risk. Risk embarrassment, risk something when you speak up; you had to kind of get over that mental hurdle – for me that was a bit of an issue – I know many people - men and women - for whom that is a personal issue, but it seems to be more common with women, right? So I think that’s a key thing - just willing to be wrong. Speaking up with confidence.

How did you choose this field?

I kind of fell into majoring in math as an undergraduate. I changed my major about ten times – really couldn’t figure out what I wanted to do, and in the end I happened to major in math just because I happened to take all the classes. I couldn’t figure out what else to do. And then I really got more deeply into it when I fell into a project that involved network modeling for the US postal service. And that was the first time I really fell into this actual modern space and fell in love with this idea of using patterns and identifying patterns. I’ve always been a pattern junkie! I love patterns of any kind and then I realized ‘Oh there’s this field called Operations Research” which is very much about optimization. And so from there, I just stayed in the field.

What do you mean when you talk about stewardship of data? About being mindful about being custodians of data?

What I mean is that, we are fortunate. We should all be so grateful that we can get our hands on so much data. It wasn’t always the case. Privacy used to be the sacred thing. Now you look at the world and I can’t believe how much my kids share on Facebook. People are so open. With that openness has come an openness of data and of course the Internet being the main thing that’s giving us so much data. And I just think that it’s easy to forget that’s a gift and something to be careful with.
 
Legalities help a lot. Certainly the law helps to protect core things about data. But I believe personally that it goes beyond that. Most people don’t realize how much of their data is actually out there. And we shouldn’t take advantage of the fact that they don’t recognize that. We should care for the data as if  they don’t know. And so from that perspective, doing positive things with the data, being very careful not to misuse the data, being careful not to use some people’s data in order to help a different population of people – I think that’s very wrong. So not using people but having positive intentions to give back towards the people who have contributed.

Data science as a career

It’s so exciting. Such an amazing kind of career. One thing I love about it that I don’t kind of think is recognized enough is that how creative of a field it is. It’s not just a technical field – that’s what makes it so exciting. It’s extremely creative because you’re thinking about how data might fit together, you’re thinking about how you might create different signals from the data. My advice is 'Do It' – it’s a fantastic career. Try to get a variety of experience, there’s so many aspects to data science. I think it’s great to  do things where you are doing statistical analyses versus building predictive models – you know that array of experience could be super useful early on in a science  career.

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"Be willing to take a personal risk; be willing to be wrong".

Caitlin Smallwood says women must be willing to risk embarrassment to get ahead in a career; even if you sometimes get it wrong… speaking up builds confidence.
Caitlin is VP of Science & Algorithms at Netflix which has about 69 million customers. She spoke to TC about protecting data privacy, and how her love for math makes her ‘a pattern junkie’.

What is your role at Netflix?

I lead a group that’s a data science group. It's called Science & Algorithms at Netflix. We do everything ranging from algorithm research and development to predictive models that are leveraged internally for decision making, and all the experimentation … AB testing kind of methodologies, etc.

How does your work impact the Netflix consumer?

Ah well! Hopefully it means that the product is getting better and better for you and that the recommendations that you are seeing on the service are titles that you would enjoy watching, and hopefully, that’s getting better over time such that if you were a member of Netflix a year from now, your recommendations are even more perfect for you and letting you find great stuff. It’s helping us get content into the service that you will really enjoy. It’s helping to inform our content plan around what types of content do you want to satisfy everyone’s tastes.

What are the challenges facing women entering your field? Are there fewer women in data science?

I think it’s about the same for data science as in any deeply technical field where we face the kind of challenges you’ll see differentially during the course of people’s career evolution. If you look at people who enroll in programs I think you’ll find that it's almost 50 50 in those early stages and then as people progress through their careers, women do drop off at each sort of level.  So that’s the area for all tech fields - it’s really important to try to make the career more accessible over time.
Part of it is the natural thing of how people want to handle the phase of their lives when they’re having kids – that’s a challenge, right? And a lot of that (as many people have written about) has to do with society shifting so that more men take on more responsibility during that time. It’s certainly also about other things – like companies and what they can do to make sure the environment is flexible  a practical environment for women to be in their careers, during those times.

How would you describe the transitions in your own professional development in choosing to pursue your career?

I think that for me, personally, there was a phase of reaching a really different level of confidence that was really important. The ability to take personal risk. Risk embarrassment, risk something when you speak up; you had to kind of get over that mental hurdle – for me that was a bit of an issue – I know many people - men and women - for whom that is a personal issue, but it seems to be more common with women, right? So I think that’s a key thing - just willing to be wrong. Speaking up with confidence.

How did you choose this field?

I kind of fell into majoring in math as an undergraduate. I changed my major about ten times – really couldn’t figure out what I wanted to do, and in the end I happened to major in math just because I happened to take all the classes. I couldn’t figure out what else to do. And then I really got more deeply into it when I fell into a project that involved network modeling for the US postal service. And that was the first time I really fell into this actual modern space and fell in love with this idea of using patterns and identifying patterns. I’ve always been a pattern junkie! I love patterns of any kind and then I realized ‘Oh there’s this field called Operations Research” which is very much about optimization. And so from there, I just stayed in the field.

What do you mean when you talk about stewardship of data? About being mindful about being custodians of data?

What I mean is that, we are fortunate. We should all be so grateful that we can get our hands on so much data. It wasn’t always the case. Privacy used to be the sacred thing. Now you look at the world and I can’t believe how much my kids share on Facebook. People are so open. With that openness has come an openness of data and of course the Internet being the main thing that’s giving us so much data. And I just think that it’s easy to forget that’s a gift and something to be careful with.
 
Legalities help a lot. Certainly the law helps to protect core things about data. But I believe personally that it goes beyond that. Most people don’t realize how much of their data is actually out there. And we shouldn’t take advantage of the fact that they don’t recognize that. We should care for the data as if  they don’t know. And so from that perspective, doing positive things with the data, being very careful not to misuse the data, being careful not to use some people’s data in order to help a different population of people – I think that’s very wrong. So not using people but having positive intentions to give back towards the people who have contributed.

Data science as a career

It’s so exciting. Such an amazing kind of career. One thing I love about it that I don’t kind of think is recognized enough is that how creative of a field it is. It’s not just a technical field – that’s what makes it so exciting. It’s extremely creative because you’re thinking about how data might fit together, you’re thinking about how you might create different signals from the data. My advice is 'Do It' – it’s a fantastic career. Try to get a variety of experience, there’s so many aspects to data science. I think it’s great to  do things where you are doing statistical analyses versus building predictive models – you know that array of experience could be super useful early on in a science  career.

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