Luc Goutermanoff

I have an engineering degree (eq. master’s degree) of applied mathematics and computer science with a specialization in financial engineering from both KTH (Stockholm, Sweden) and ENSIMAG (Grenoble, France)

In 2020/2021, I completed the Deep Learning Specialization from Coursera and I participated in the course “Programa de Repensar la Sostenibilidad y la Inclusión Social” from Universidad Torcuato Di Tella.

I am proposing consulting services, but I am open to exploring full-time opportunities.

English CV, Spanish CV, and Linkedin.

---

Luc’s glossary (as of February 2021, updates needed)

or a attempt to describe my experience in a more detailed and sincere way.

---

# values

That’s good to think about what are my “values”. Heuuu, let’s say:integrity, value-oriented, efficiency with pragmatism, transparency, engagement, passion, social impact,

# energy markets: electricity, gas, oil

They have accompanied me during a great part of my professional career, be it the electricity market in Europa, the gas & oil market in Europa, or South America. I have a good understanding of the fundamentals which drive these markets, and how they relate to each other. I can model them, proposing future evolutions based on historical variations or market information. I consider that a useful model is a right balance between the deterministic part estimation (seasonality, …) and the stochastic part estimation (volatility, correlation).

For an actor of these markets, it is fundamental to estimate accurately the stochastic part, which will be a key input in the market risk estimation. Then, depending on the shape of the market risk, it can be hedged with one of many products, be they financial or physical. If the perfect hedge doesn’t exist, a proxy hedge may eventually be a solution.

The challenge of these markets is their diversities I would say. While they all belong to the same family, under the physical definition of what energy is, their market structure might have very distinct specificities (market design, regulation, …), even for a subfamily of similar energies.

# agriculture market

I have a good knowledge and understanding of them I think but based on indirect experience. I had a very nice experience in parametric insurances, ie focused on the climate exposure (drought, flood) of the crop farmer (soybean, corn, wheat). From this, I came to understand much more, the crop cycle from planting to harvesting, the hail insurance, the structure of the global agriculture market.

I have worked on projects for Argentina of course, but also Brazil and the USA.

# programming skills

I love the Eric’s Weber article “Being an expert is rare. I’m not one. “.

I consider I am proficient in the following language: C#, C++, python, SQL, though it may differ between them. You can see here something of my code. For now, there are few, because most of my coding experience has been made within the companies I worked in. But I plan to keep on playing at open data challenge, etc…

# ETRM/CTRM

They stand for “Energy Trading and Risk Management” and more generally “Commodity Trading and Risk Management“. They are used in general in the software industry to define the ones used in energy/commodity trading. I have worked on – use them, implement them, debug them, … – many different kinds, be it in-house software, be it off-the-shelf software, be it also quick dll implementation, with Excel interface.

# physical asset optimization, flexibility valuation (in # energy market)

How to make the best decisions when using physical assets, be it a power generation fleet, a gas storage, a take-or-pay contract? What if you need to consider the supply of a client portfolio which is weather dependent? When markets are volatile, when you are not confident in some physical parameters on the production side or the supply side… Something I really enjoy working on, with this connection between the abstract financial market and the reality of physical assets through applied mathematics. I had the feeling that it ends up being artistic decisions sometimes, with many uncertainties hardly quantifiable. But I always believed that improving the understanding of the decision is worthful.

# risk management

… in commodities market then. This keyword somehow gets along my entire experience, but I have the feeling that it has so many definitions and practices here, there and everywhere, that it generates confusion more than it helps.

On my side, it would be something such as estimating the risk of potential losses, due to market or weather variations, be it extreme event or not. And providing the apropriate tool to measure and visualize how concrete may be this risk. Note that this can involve complex calculations and IT infrastructures, depending on the size of the company.

Returning to the conceptual level, once you have estimated it, you need to to take into account the uncertainty that remain in your estimation (a whole debate…), then it leads to optimal decision within your portfolio (see #physical asset optimization). Or you can eventually get a price for it and transfer it to third party. I believe it is fundamental to establish a risk framework which is understood by all the relevant stakeholders and avoid this easy understand of only “avoiding losses”. Hence, the pedagogical part of the work is essential. I tend also to think that it is necessary to break down the silos in a company if you expect to take advantage of the risk management protocol you are setting up.

Applying risk management concept in commodity provides some exciting experience I believe, with all the flexibility valuation (power plants, swing option, gas storage),

# weather derivatives

In energy market, with derivative contracts based on market prices along with a weather parameter like the temperature from given weather station.

In agriculture, with parametric insurance to price insurance against flood, drought. It was index-based using satellite data.  Here also, I did really enjoy the connection between the abstract financial/insurance market with the reality of weather through applied mathematics.

# data science

Fundamental keyword, no? what is this all about? I have studied statistics (and applied it). Today, I am applying something of ML, DL. I think it is a overrated. Concretely, there is some situations in which advanced algoritms may solve problems in a efficient manner, but in most of the situations, business problems may be solved with basic algorithm or statistic.

I believe also there is much value in an efficient data management, in an agile and transparent work culture. This may be also very complex to solve, not sure ML, DL may be of some help here. This article expresses my feeling very well, see in particular the organizational culture part: lack of data-driven culture, inefficient and slow IT and data governance processes, … That’s crazy how these things are similar here, there and everywhere. But that’s life, this is a challenge to solve, patiently. I believe it is essential to take into account the human part in the digital transformation that data science may generate, to get things done efficiently.

# energy markets: electricity, gas, oil

They have accompanied me during a great part of my professional career, be it the electricity market in Europa, the gas & oil market in Europa, or South America. I have a good understanding of the fundamentals which drive these markets, and how they relate to each other. I can model them, proposing future evolutions based on historical variations or market information. I consider that a useful model is a right balance between the deterministic part estimation (seasonality, …) and the stochastic part estimation (volatility, correlation).

For an actor of these markets, it is fundamental to estimate accurately the stochastic part, which will be a key input in the market risk estimation. Then, depending on the shape of the market risk, it can be hedged with one of many products, be they financial or physical. If the perfect hedge doesn’t exist, a proxy hedge may eventually be a solution.

The challenge of these markets is their diversities I would say. While they all belong to the same family, under the physical definition of what energy is, their market structure might have very distinct specificities (market design, regulation, …), even for a subfamily of similar energies.

# agriculture market

I have a good knowledge and understanding of them I think but based on indirect experience. I had a very nice experience in parametric insurances, ie focused on the climate exposure (drought, flood) of the crop farmer (soybean, corn, wheat). From this, I came to understand much more, the crop cycle from planting to harvesting, the hail insurance, the structure of the global agriculture market.

I have worked on projects for Argentina of course, but also Brazil and the USA.

# programming skills

I love the Eric’s Weber article “Being an expert is rare. I’m not one. “.

I consider I am proficient in the following language: C#, C++, python, SQL, though it may differ between them. You can see here something of my code. For now, there are few, because most of my coding experience has been made within the companies I worked in. But I plan to keep on playing at open data challenge, etc…

# ETRM/CTRM

They stand for “Energy Trading and Risk Management” and more generally “Commodity Trading and Risk Management“. They are used in general in the software industry to define the ones used in energy/commodity trading. I have worked on – use them, implement them, debug them, … – many different kinds, be it in-house software, be it off-the-shelf software, be it also quick dll implementation, with Excel interface.

# physical asset optimization, flexibility valuation (in # energy market)

How to make the best decisions when using physical assets, be it a power generation fleet, a gas storage, a take-or-pay contract? What if you need to consider the supply of a client portfolio which is weather dependent? When markets are volatile, when you are not confident in some physical parameters on the production side or the supply side… Something I really enjoy working on, with this connection between the abstract financial market and the reality of physical assets through applied mathematics. I had the feeling that it ends up being artistic decisions sometimes, with many uncertainties hardly quantifiable. But I always believed that improving the understanding of the decision is worthful.

# risk management

… in commodities market then. This keyword somehow gets along my entire experience, but I have the feeling that it has so many definitions and practices here, there and everywhere, that it generates confusion more than it helps.

On my side, it would be something such as estimating the risk of potential losses, due to market or weather variations, be it extreme event or not. And providing the apropriate tool to measure and visualize how concrete may be this risk. Note that this can involve complex calculations and IT infrastructures, depending on the size of the company.

Returning to the conceptual level, once you have estimated it, you need to to take into account the uncertainty that remain in your estimation (a whole debate…), then it leads to optimal decision within your portfolio (see #physical asset optimization). Or you can eventually get a price for it and transfer it to third party. I believe it is fundamental to establish a risk framework which is understood by all the relevant stakeholders and avoid this easy understand of only “avoiding losses”. Hence, the pedagogical part of the work is essential. I tend also to think that it is necessary to break down the silos in a company if you expect to take advantage of the risk management protocol you are setting up.

Applying risk management concept in commodity provides some exciting experience I believe, with all the flexibility valuation (power plants, swing option, gas storage),

# weather derivatives

In energy market, with derivative contracts based on market prices along with a weather parameter like the temperature from given weather station.

In agriculture, with parametric insurance to price insurance against flood, drought. It was index-based using satellite data.  Here also, I did really enjoy the connection between the abstract financial/insurance market with the reality of weather through applied mathematics.

# data science

Fundamental keyword, no? what is this all about? I have studied statistics (and applied it). Today, I am applying something of ML, DL. I think it is a overrated. Concretely, there is some situations in which advanced algoritms may solve problems in a efficient manner, but in most of the situations, business problems may be solved with basic algorithm or statistic.

I believe also there is much value in an efficient data management, in an agile and transparent work culture. This may be also very complex to solve, not sure ML, DL may be of some help here. This article expresses my feeling very well, see in particular the organizational culture part: lack of data-driven culture, inefficient and slow IT and data governance processes, … That’s crazy how these things are similar here, there and everywhere. But that’s life, this is a challenge to solve, patiently. I believe it is essential to take into account the human part in the digital transformation that data science may generate, to get things done efficiently.

I have participated in a data challenge competition and ranked 31 over more than 120. I guess I need to practice more. As said, I completed a deeplearning specialization from Coursera. This allowed me to lift the veil on this trendy topic.

# ESG metrics

Environment, Social and Governance. I participated in a sustainable development course where I was given a presentation of this metric. As Wikipedia says, they are the three central factors in measuring the sustainability and societal impact of an investment in a company or business.

I would like to understand deeply this topic. Mainly environment but also social impact. I believe market/finance need to reconnect with the reality of our society and this approach is on a good try. I guess it is challenging – but essential – to do it efficiently. It may be tempting to talk the talk without walking the walk (including for me!) and I would be happy to do my bit there somehow.

# problem solving

or alternatively, critical thinking. Sincerely, I consider myself as a problem solver. I enjoy looking at a given problem from many sides to solve it in an efficient manner. In my opinion, the essential skills to be a good problem solver are the curiosity and the perseverance, with an ounce of resilience and lateral thinking. I think my definition is in line with the design thinking mindset.

(I think that our mums are some of the best problem solvers)

or alternatively, critical thinking. Sincerely, I consider myself as a problem solver. I enjoy looking at a given problem from many sides to solve it in an efficient manner. In my opinion, the essential skills to be a good problem solver are the curiosity and the perseverance, with an ounce of resilience and lateral thinking. I think my definition is in line with the design thinking mindset.

(I think that our mums are some of the best problem solvers)

# automatization

I have worked in working environment with this surrounding. Agile method, … platform, sharing code

# teamwork

I love this feeling of team working, when you feel you’re trust by the others, and you trust them. It does not always occur. I suppose we all share our part when the teamwork does not work. On my side, I consider the following skills as important to get a nice collaborative atmosphere:

• being authentic
• self-awareness
• integrity
• active listening

# fear

I am afraid of today’s incertitude, the one surrounding me, emerging from the VUCA world in which we are. I think it is healthy to accept it.

# consistency

I need consistency, between my values and my actions, observing the challenge of conciliating both, sometimes.

# 2020 quarantine

I have spent much time with my kids. That was awesome.

# design thinking mindsets

• Reframing problem
• Mindfulness of process
• Curiosity
• Radical collaboration
• Bias towards action
• Cultural barriers are many :
  • social barriers : engineers eat lunch with engineers.
  • Semantic gaps:  people use different words small diffrences matters
  • Conceptual blocks: impact the universe of perceived actions

(See Stanford video)

# VUCA

Volatility, uncertainty, complexity, ambigu