Self Employed

Data Science & Analytics Advisor for Growth Leaders. 

nemo

Advisor

Rengage

Otrium

+ Launched Otrium's experimentation practice
+ Identified and implemented A/B testing vendor
+ Built first global customer segmentation

Product Growth Consultant

+ Lead Performance Marketing for both acquisition and retention strategies.
+ Contributing to 1st European EBITDA break even month in November
+ Increased new customers 40% YoY in US with flat CACs
+ Launched first full funnel campaigns in US
+ Improved Global ROAS 28% YoY

Interim Head of Performance Marketing

We Are Eves

+ Improved activation rate 15% by surveying customers to identify product gaps

Sr. Product Growth Manager

Scenset

+ Boosted trip bookings 25% through behavioral analyses and product recommendations
+ Improved core product adoption 80% by innovating on main user flow 
+ Increased MRR from $0 -> $100K by launching A/B testing framework
+ Implemented data warehouse using Big Query, R, and Data Studio

Director of Product Experimentation and Analytics

Uber

Oversaw Global Brand and International Growth Marketing Data Science for all consumer verticals
Managed 8 data scientists + 5 contractors located across all major timezones

+ Launched Uber's first Brand measurement and experimentation practice
+ Owned measurement for $1Bn in Brand Marketing spend

Head of Brand Data Science (Promotion)

Oversaw International Growth Marketing Data Science in all non-US markets for all consumer verticals
Managed 5 analysts + 5 contractors located across all major timezones

+ Designed a global Covid recovery segmentation used to target 20M users with $3M+ in rider discounts
+ Delivered $150M of incremental Gross Revenue through geographic experiments

Head of International Growth Marketing Data Science (Promotion)

Manage career development, priorities, and work load for 3 junior data scientists

+ Drove 5% incremental trips and 7% incremental gross bookings by optimizing email targeting
+ Saved $26M in yearly rider marketing spend by cutting inefficient marketing channels 
+ Improved ROAS 50% through incrementality tests and channel optimization 
+ Increased CVR 2% through improved segmentation

Rider Growth Marketing Data Science Lead (Promotion)   

+ Added 10% incremental trips by managing weekly $500K promotion budget targeting high value riders
+ Reduced support costs $4M by leading a global effort to quantify product defects 
+ Increased 3-month driver retention 3% by improving email recommendations

Rider Growth Data Scientist

KIMO

DIRECTV

Growth Marketing Data Science Consultant  

U.S. Department of the Treasury

Debt Manager, Office of Debt Management

All growth leaders know the benefits of being data-driven. But most are frustrated by the current way they work with Data Science & Analytics teams. They want more insights and proactivity. 

This happens when one of 3 things is broken:
— Process
— People
— Focus

Are you structured and partnering in the right way?

Do you have the right skill set?

Are teams aligned on the strategy?

This is where my consulting practice comes in. I do one thing:

I help growth leaders hit targets by optimizing how you work with Data Science & Analytics teams.

I facilitate concrete and actionable changes that start showing results within weeks, but are scalable and sustainable for the long term.

I was fortunate to be at Uber for 5+ years during their hyper-growth phase and then a few promising early stage startups. Having built and scaled multiple growth data science teams, adding $100m+ in incremental value, has given me the rare ability to deeply understand stakeholder needs while simultaneously mentoring Data Scientists & Analysts to improve performance. 

When I'm not working, I'm spending time with my amazing wife and toddler cooking, traveling, playing chess, and just enjoying life!

You might be data driven, but life is not. 

The past 3 weeks, I’ve experienced 3 life events that each were low probability.

The fact they happened within 3 weeks is just absurd.

But none of that matters. 

Life isn’t about numbers .

It’s about uncountable things:
- Love
- Family
- Laughter 
- Memories 
- Friendship

I’ll be continuing to take the month to process and hope to continue on the momentum I had before.

Sundar Swaminathan

Data Science & Analytics advisor to scale up and enterprise growth leaders.

March was a month from hell.

It sucked but writing about it has been therapeutic. 

This one is not fun or uplifting.

Check it all out and more below 👇

Thank you to the 1222 subscribers who follow along on my journey and to everyone else that takes time from your busy schedules to read. This month's c

Open article: Chapter 12: The human side by Sundar Swaminathan • 4 min read

Sundar Swaminathan on LinkedIn • 4 min read

Data Science & Analytics teams are in a vicious spiral.

Let me know if this sounds familiar to you:

A company thinks they need a Data Analyst / Scientist. 

"That will solve all of our problems"

They then go hire a couple without the right structure in place.

These analysts and scientists are treading water while growth teams put on more and more on their shoulders thinking these analysts should be able to fix everything.

Then these analysts and scientists don't perform to expectations.

The company then decides these data teams are ROI negative.

They cut them.

Performance continues to suffer and actually now gets worse.

The reality is that Data teams should be ROI positive.

If their impact doesn't materialize, it's because something is broken.

Either you've hired the wrong people or your process is broken or they're working on the wrong things.

I've seen this soooooooooooo many times.

Before you cut your Data Analytics & Science teams, see if you can make them ROI positive.

If you can't, bring in someone like me that does exactly that. 

Part 3 of reinventing my consulting practice:

Figuring out what I love doing.

This was surprisingly easier than I imagined.

I was able to measure what I love doing by just thinking about doing that act.

I found that I had a very noticeable emotional, mental, and physiological reaction to the thought of something and I could tell if I loved doing it. 

But, I had to really push myself. I had to LOVE doing it. 

Not like. Not really like. LOVE. 

It turns out there is 1 important thing I don't love doing:

IC Work. 

I don't think I'm too good for it.  

I just don't love doing it.

As I dove deeper and deeper into why I don't love doing, I unlocked another important qualification mechanism:

What am I uniquely good at?

Side note: The reality is that you're rarely going to be the ONLY person in the world that's good at something. FYI, Lebron James was born within months of Michael Jordan starting to play in the NBA. These are 2 once in a lifetime players. 

So, thinking along the lens of what am I uniquely good at, I reflected again on feedback, articles I've read, and conversations I've had.

As a career Data person, I can confidently say I'm uniquely good at communicating with leaders. Verbal, non-verbal, in-person, decks, speeches, etc. 

I wasn't always good at it, but I identified that as a skillset that could could set me above everyone else and then I worked on it.

So, now I knew I didn't like doing IC work (because I'm not the only one uniquely good at) and I love to network, connect, and convert insights into action at the executive level. 

I then thought about whether I could do that for the next 30 years of my life. It had to sustainable.

My answer is yes. If I had to go into exec meetings, board rooms, and other high stake places and present insights, I would LOVE it. 

So, after some deep thought I now realized what I loved doing and how I was trying to do things I didn't love which burned me out.

Stay tuned as I share how I discovered what my clients need and how I came up with my offerings. 

Part 2 of reinventing my consulting practice: 

Figuring out what I'm good at. 

This was surprisingly harder than I imagined.

Part of it is cultural.

Society doesn't love people that brag about their work.

The distinction between confident and cocky is constantly moving, vague, and  arbitrary.

The second reason it's hard is because there is no objective measure.

For example, how do I quantify that I'm good at SQL? 

I took the approach of deeply reflecting on what others wrote about me in feedback reviews across managers, peers, and my direct reports over my career.

While not objective, it gave me an approach that made sense. 

What do people "perceive" that I'm good at.

I then tried to balance between being specific and broad. Specific so that I could really deeply understand what I'm good at but broad so that I ensured I covered all parts of that skillset.

Example, I'm good at left joins in SQL is stupid. It's too specific and doesn't mean I'm good at SQL. 

Saying I'm good at SQL made me question if I was good at writing SQL, researching SQL when I had questions, understanding concepts, and being able to help others. 

Here's the list of things I'm very good at:
— Creating a partnership with stakeholders & leaders
— Product & Marketing experimentation strategy
— Networking and making connections
— Simplifying complex analyses 
— Unlocking Marketing Insights
— Unlocking Product Insights
— Presenting to executives
— Empowering analysts
— SQL

These topics felt specific enough but also all encompassing to ensure I was good at every part of it.

This is my list. What do you think? Anything you'd add (for those that know me well enough)?

P.S. I originally said I started with figuring out what I love but actually started with what I'm good at because everything I love doing I'm good at but I don't love doing everything I'm good at. 

Part 5 of reinventing my consulting practice:

Finalizing my offerings and value add. 

It was pretty obvious I was targeting the wrong audience. 

Once I listed what I'm good at, what I love doing, and what my audience's needs are, it was very apparent I had been wasting time.

I highlighted the rows where there's overlap across all 3 attributes. 

There were only 4 areas of overlap in early stage startups I was going after.

The later stage companies had 7 areas of overlap.

In addition, between early and late stage only 1 skillset was being dropped and replaced so I was still able to do the same things I loved in late stage that I was doing in early stage (but vice versa was not true) 

Mathematically I could add at least 75% more value to later stage companies.

Now, my "Tagline". 

This isn't that important but it helped me summarize what I do. 

It took me a few iterations but I think I've gotten it:

"I help scale up and enterprise growth leaders hit targets by maximizing the output of Data Science & Analytics teams"

Now I have a mission and elevator pitch wrapped up in one. I want to help both Growth Leaders AND Data Science & Analytics teams realize the true value and potential of that collaboration. 

So, the next step was to outline some base offerings. I saw a great article by Mike Wilner around offerings (link here: https://lnkd.in/gjQzvBj9) 

It really helped me think through what I wanted to put together. I started by focusing on two offerings that a Growth leader would think of as a no brainer. These directly solved 2 problem areas they could have. 

1) Org design - this is a no brainer if you're planning to invest hundreds of thousands in DS & DA , but likely have never build a data team yourself. 

2) ROI Assessment - if you're unhappy with your current Data Science & Analytics collaboration then I would help identify areas where you could increase ROI. Obviously I'd have actionable recommendations as follow ups. 

These seemed like good starting points and I have confidence that I wanted to actually deliver on these when I sell them. 

That's the part that was holding me back before. I was trying to pitch work I didn't even want to do. 

So, now that I know my offerings and value add, there's only one step left:

RELAUNCH! 

Stay tuned for next week :) 

Math suggests a seasoned analyst is 16x more efficient than an unexperienced one. Here's the proof: 
 
2x better at prioritizing 
 
2x better at scoping & executing 
 
2x better at presenting 
 
2x better at managing stakeholders 
 
Obviously, it's not an exact science , but this is in line with the logic of a "10x programmer". 
 
The best part for hiring managers? 
 
You're not paying 16x the salary. 
 
At most, you're paying 2-3x for full time. 
 
At least, you're paying .5 - 1x for fractional. 
 
So, if you're hiring your first growth analysts, I'd recommend a more experienced one. 
 
Especially at a fast moving startup, stakeholder management and prioritization are crucial. 
 
P.S. Obviously a biased post, but math is math, so if you're looking for your first growth analyst, let's chat.

A few weeks ago, I shared my struggles on being a solopreneur which resonated with many people.

I feel like I have a bit more clarity now. 

Over the next few days / weeks, I'll share the exercises I did and the results.

Hopefully it can help some of you that are struggling too.

Here's a rough outline of what I'll share:

1. The process I went through
2. What I'm good at 
3. What I love doing
4. What my clients need
5. What my offerings are

The past 3 months as a solopreneur have been the most frustrating and demotivating. And this is not one of those "but something magic happened after" posts. This is therapy through building publicly.

I saw Elena Verna's post and realized I'm not using my superpower. 

More importantly, I'm trying to do work I would label as "Mentally Draining" and that's exactly how I feel. 

It sucks. It definitely is draining. It's demotivating. It's frustrating.

If you've been following me, you'll have seen that my banner and profile tagline have changed 100x times.

That's not me A/B testing to optimize. That's me A/B testing to find my identity.

Every time I talk to someone, I change it because I get a new piece of information or they say something that makes me go oh yeah that's how I want to position. 

The problem is most of my immediate network is early stage. They're all fractional executives who love building in the 0->1 phase. They're motivated by that. I thought I was too. I'm not.

But I don't have a clear view of what I do want to do.

I've worked at Uber when it was hyper hypergrowth but it was already "valued" at $50bn in 2016. 

I've worked at two preseed/seed stage startups. My last client was a Series C.

I spent 5+ years in Growth Marketing Analytics. Then I worked 2 years in Product Analytics.

I've worked in Mobility, Travel, Media, US Government, Beauty & Wellness, and now I advise a B2B SaaS. WHAT THE F*CK DO I WANT TO DO?

And the pace at which I'm changing my identity is unhealthy. It's literally changed almost every day in the past 2 weeks.

If you add the fact that I haven't found a client (a symptom of my problem) in 3 months then the doubt + financial pressures kick in and it's hard not to start spiraling. The funny part is it's easier for me to tell people I have decent runway (which is true) but in the micro moments, I keep forgetting that.

So, I'm going to take the next few days to step away from LinkedIn and do some deep work on figuring out two things:
— Things I'm good at
— Things I love doing 

I'll see y'all next week! 

Part 4 of reinventing my consulting practice:

Figuring out what my clients need. 

This is always going to be a bit of a trial and error until I get enough data points to validate. 

First, I started with looking at activities that I am good at.

Why? That's the only way I can add value. 

Then, without thinking about what I love doing, I looked at what my clients needed.

Now, here's the tricky part. 

What if you don't know who your ICP is?

Well, turns out this exercise was perfect for that.

In Data, there are generally capabilities and priorities associated with each stage of a company.

For example, a preseed stage doesn't need advanced segmentation.

So, I made two different scenarios. One earlier stage and one later stage. These "stages" should be mutually exclusive and collectively exhaustive (MECE). 

The more defined you can make your stages of the company the better, but for now I kept it high level. 

I also didn't narrow down WHO I had to sell to within those companies. 

I just wanted to get a lay of the land of what type of companies I should be selling to. 

In addition, I asked the question "Does a company need a SENIOR DATA LEADER to do this?". 

There are cheaper alternatives for certain tasks. 

I went down the list and only marked yes in places I had evidence that companies absolutely needed a Senior Data Leader to do that task.

Now I had a complete picture of:
— What I'm good at doing
— What I love doing
— What my clients needed me to do

Stay tuned as I'll share tomorrow how I finally was able to find my niche, value add, tagline etc. 


Part 1: The process

As I started my journey to figure out myself, I had a flood of suggestions on a process to use: Ikigai. 

Ikigai is a Japanese term that roughly translates to "Reason for being".

It's an exercise to discover your purpose and is part of the reason why the Japanese have one of the highest rates of centenarians.

When you have a purpose to being, you are motivated to continue being. 

This philosophy adopted for business founders includes 4 pillars:
- Passion: what do you love doing
- Skill: what are you good at doing
- Market: what does the market need
- Viability: how can I make it sustainable

This is exactly what I did. For a week, I got off LinkedIn and just really thought about all 4 of these points.

But where do you start?

I started with Passion and Skill.

Viability can come later and that's the part that you can continuously experiment with. 

You can also adjust your business models over time. Not everything needs to be a unicorn. 

Market also requires time to figure out but if you don't align on passion and skill then how do you know who to talk to for validation? You don't.

But if you experiment with Passion and Skill then your foundation is just unstable.

You'll continuously go back and forth burning yourself out on activities that you don't love doing or that you're not good at.

So, I started with Passion and Skill.

Tomorrow, I'll share the things I'm passionate about and how I discovered I was passionate about them. 

Every growth leader I talk to works with Data Analysts / Data Scientists. 
But, most of them aren't completely satisfied. 

They wan't more insights and more proactivity. 

I can't tell if this is a micro issue just within my network or a broader issue.

What do you think? Are you happy with your partnership?

Are you happy with your partnership with Data Science / Analytics teams?

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